Method and system for resolving hemisphere ambiguity using a position vector

ABSTRACT

Embodiments resolve hemisphere ambiguity at a system comprising sensors. A hand-held controller of the system emits magnetic fields. Sensors positioned within a headset of the system detect the magnetic fields. A first position and orientation of the hand-held controller is determined within a first hemisphere with respect to the headset based on the magnetic fields. A second position and orientation of the hand-held controller is determined within a second hemisphere, diametrically opposite the first hemisphere, with respect to the headset based on the magnetic fields. A normal vector is determined with respect to the headset, and a position vector identifying a position of the hand-held controller with respect to the headset in the first hemisphere. A dot-product of the normal vector and the position vector is calculated, and the first position and orientation of the hand-held controller is determined to be accurate when a result of the dot-product is positive.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/702,339 filed Jul. 23, 2018 titled “SYSTEMS AND METHODS FORAUGMENTED REALITY,” the entire disclosure of which is herebyincorporated by reference in its entirety, for all purposes, as if fullyset forth herein.

This application is related to U.S. patent application Ser. No.15/859,277, filed on Dec. 29, 2017, entitled “SYSTEMS AND METHODS FORAUGMENTED REALITY,” the entirety of which is hereby incorporated byreference.

FIELD OF THE INVENTION

The present disclosure relates to systems and methods to localizeposition and orientation of one or more objects in the context ofaugmented reality systems.

BACKGROUND OF THE INVENTION

Modern computing and display technologies have facilitated thedevelopment of systems for so called “virtual reality” or “augmentedreality” experiences, wherein digitally reproduced images or portionsthereof are presented to a user in a manner wherein they seem to be, ormay be perceived as, real. A virtual reality, or “VR”, scenariotypically involves presentation of digital or virtual image informationwithout transparency to other actual real-world visual input; anaugmented reality, or “AR”, scenario typically involves presentation ofdigital or virtual image information as an augmentation to visualizationof the actual world around the user.

Despite the progress made in AR and VR systems, there is a need for abetter localization system in the context of AR and VR devices.

SUMMARY

The present invention relates to systems and methods to optimallyinterpret data input from multiple sensors; in other words, embodimentsdescribed herein refine multiple inputs into a common coherent outputwith less computational resources than used to correct data input from asingle sensor input.

In one embodiment, a computer implemented method for resolvinghemisphere ambiguity at a system comprising one or more sensors includesemitting, at a hand-held controller of the system, one or more magneticfields. The method further includes detecting, by one or more sensorspositioned within a headset of the system, the one or more magneticfields. The method also includes determining a first position and afirst orientation of the hand-held controller within a first hemispherewith respect to the headset based on the one or more magnetic fields,and determining a second position and a second orientation of thehand-held controller within a second hemisphere with respect to theheadset based on the one or more magnetic fields. The second hemisphereis diametrically opposite the first hemisphere with respect to theheadset. The method also includes determining a normal vector withrespect to the headset, and a position vector identifying a position ofthe hand-held controller with respect to the headset in the firsthemisphere. In addition, the method includes calculating a dot-productof the normal vector and the position vector, and determining that thefirst position and the first orientation of the hand-held controller isaccurate when a result of the dot-product is positive. The method alsoincludes determining that the second position and the second orientationof the hand-held controller is accurate when the result of thedot-product is negative.

In one or more embodiments, the position vector is defined at acoordinate frame of the headset. The normal vector originates from theheadset and extends at a predetermined angle from a horizontal line fromthe headset. In some embodiments, the predetermined angle is 45° angledown from the horizontal line from the headset. According to someembodiments, when the result of the dot-product is positive, the firsthemisphere is identified as a front hemisphere with respect to theheadset and the second hemisphere is identified as a back hemispherewith respect to the headset. In some embodiments, when the result of thedot-product is positive, the first hemisphere is identified as a fronthemisphere with respect to the headset and the second hemisphere isidentified as a back hemisphere with respect to the headset. In one ormore embodiments, the system is an optical device. According to someembodiments, the method is performed during an initialization process ofthe headset.

In some embodiments, the method also includes delivering virtual contentto a display based on: the first position and the first orientation whenthe result of the dot-product is positive; or the second position andthe second orientation when the result of the dot-product is negative.

In some embodiments, data input from a first sensor is updated by acorrection data input point from a second sensor. As noisy data iscollected, such as by a high frequency IMU, it is periodically updatedor adjusted to prevent excessive error or drift from negativelyaffecting system performance or interpretation of that data.

In some embodiments, a first sensor's inputs are reset to originate froma corrective input point as provided by a lower frequency and moreaccurate second sensor, such as radar or vision system. These moreaccurate sensors are operated at lower frequency to preserve computingcycles otherwise necessary to operate them at full capacity, as theirinput need only be to periodically ground, or update and correct thenoisier data the lower frequency operation does not affect systemperformance.

In another embodiment, a system includes a hand-held controllercomprising a magnetic field transmitter configured to emit one or moremagnetic fields, a headset comprising one or more magnetic field sensorsconfigured to detect the one or more magnetic fields, and a processorcoupled to the headset configured to perform operations. The operationsinclude determining a first position and a first orientation of thehand-held controller within a first hemisphere with respect to theheadset based on the one or more magnetic fields, and determining asecond position and a second orientation of the hand-held controllerwithin a second hemisphere with respect to the headset based on the oneor more magnetic fields. The second hemisphere is diametrically oppositethe first hemisphere with respect to the headset. The operations alsoinclude determining a normal vector with respect to the headset, and aposition vector identifying a position of the hand-held controller withrespect to the headset in the first hemisphere. In addition, theoperations include calculating a dot-product of the normal vector andthe position vector, and determining that the first position and thefirst orientation of the hand-held controller is accurate when a resultof the dot-product is positive. The operations also include determiningthat the second position and the second orientation of the hand-heldcontroller is accurate when the result of the dot-product is negative.

In still another embodiment, a computer program product is embodied in anon-transitory computer readable medium, the computer readable mediumhaving stored thereon a sequence of instructions which, when executed bya processor causes the processor to execute a method for resolvinghemisphere ambiguity at a system comprising one or more sensors includesemitting, at a hand-held controller of the system, one or more magneticfields. The method further includes detecting, by one or more sensorspositioned within a headset of the system, the one or more magneticfields. The method also includes determining a first position and afirst orientation of the hand-held controller within a first hemispherewith respect to the headset based on the one or more magnetic fields,and determining a second position and a second orientation of thehand-held controller within a second hemisphere with respect to theheadset based on the one or more magnetic fields. The second hemisphereis diametrically opposite the first hemisphere with respect to theheadset. The method also includes determining a normal vector withrespect to the headset, and a position vector identifying a position ofthe hand-held controller with respect to the headset in the firsthemisphere. In addition, the method includes calculating a dot-productof the normal vector and the position vector, and determining that thefirst position and the first orientation of the hand-held controller isaccurate when a result of the dot-product is positive. The method alsoincludes determining that the second position and the second orientationof the hand-held controller is accurate when the result of thedot-product is negative.

In some embodiments, a computer-implemented method includes detecting,based on data output by one or more sensors positioned within a headsetof a system, one or more magnetic fields emitted by an electronic devicein an environment of the system. The method also includes determining afirst position and a first orientation of the electronic device within afirst hemisphere with respect to the headset based on the one or moremagnetic fields. The method further includes determining a normal vectorwith respect to the headset, and a position vector identifying aposition of the electronic device with respect to the headset in thefirst hemisphere. The method also includes calculating a dot-product ofthe normal vector and the position vector, and determining whether thecalculated dot-product is positive or negative in value. The methodfurther includes delivering virtual content to a display of the headsetbased at least in part on determining whether the calculated dot-productis positive or negative in value.

In one or more embodiments, the electronic device comprises a hand-heldcontroller, a wearable device, or a mobile computing device. In someembodiments, in response to determining that the calculated dot-productis positive in value, delivering virtual content to the display of theheadset comprises delivering virtual content to the display of theheadset based at least in part on the first position and the firstorientation of the electronic device.

In some embodiments, the method includes determining a second positionand a second orientation of the electronic device within a secondhemisphere with respect to the headset based on the one or more magneticfields. The second hemisphere is diametrically opposite the firsthemisphere with respect to the headset. In response to determining thatthe calculated dot-product is negative in value, delivering virtualcontent to the display of the headset comprises: delivering virtualcontent to the display of the headset based at least in part on thesecond position and the second orientation of the electronic device.

In some embodiments, noisy data is adjusted by a coefficient value topre-emptively adjust incoming data points a sensor provides. As acorrective data point is received, the system “steers” the incomingnoisy data towards the corrective input point rather than completelyadjusting the noisy data to the corrective input point. Theseembodiments are particularly beneficial when there are large changes inboth sensor inputs, as a noisy data stream that steers towards acorrective input will not originate from a corrective input point in thepast that is substantially different than a current measurement wouldindicate. In other words, the noisy data stream will not originate froman obsolete corrective input point.

In some embodiments, pose prediction is made by estimating a futureposition of a user and accessing features and points expected at thatfuture position. For example, if a user is walking around a squaretable, features such as corners of the table or lines of objects on thetable are “fetched” by the system based on where the system estimatesthe user will be at a future time. When the user is at that location, animage is collected and the fetched features are projected onto thatimage to determine a correlation and determine a specific pose. This isbeneficial as it avoids feature mapping concurrent with receiving animage and reduces computational cycles by completing pre-processing ofthe fetched features (such as warping) prior to the image beingreceived, so that when the image of current pose is collected the pointscan be more quickly applied and estimated pose is refined rather thangenerated, allowing virtual content to either render at that new posemore quickly or with less jitter.

Additional embodiments, advantages, and details are described in greaterdetail below with specific reference to the following figures asappropriate.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an augmented reality scenario with certain virtualreality object according to some embodiments.

FIGS. 2A-2D illustrates various configurations of components comprisinga visual display system according to some embodiments.

FIG. 3 illustrates remote interaction with cloud computing assetsaccording to some embodiments.

FIG. 4 illustrates an electromagnetic tracking system according to someembodiments.

FIG. 5 depicts a method of electromagnetic tracking according to someembodiments.

FIG. 6 illustrates an electromagnetic tracking system coupled to avisual display system according to some embodiments.

FIG. 7 depicts a method of determining metrics of a visual displaysystem coupled to an electromagnetic emitter according to someembodiments.

FIG. 8 illustrates a visual display system comprising various sensingcomponents and accessories according to some embodiments.

FIGS. 9A-9F illustrate various control modules according to variousembodiments.

FIG. 10 illustrates a head mounted visual display with a minimized formfactor according to some embodiments.

FIGS. 11A-11B illustrate various configurations of electromagneticsensing modules.

FIGS. 12A-12E illustrate various configurations for electromagneticsensor cores according to some embodiments.

FIGS. 13A-13C illustrate various time division multiplexing ofelectromagnetic sensing according to some embodiments.

FIGS. 14-15 depict a methods of combining various sensor data uponinitiation of a visual display system according to some embodiments.

FIGS. 16A-16B illustrate a visual display system comprising varioussensing and imaging components and accessories according to someembodiments.

FIGS. 17A-17G illustrate various configurations of transmission coils inelectromagnetic tracking systems according to some embodiments.

FIGS. 18A-18C illustrate signal interference effects from various systeminputs according to some embodiments.

FIG. 19 illustrate a calibration configuration according to someembodiments.

FIGS. 20A-20C illustrate various summing amplifier configurations asbetween multiple subsystems.

FIG. 21 illustrates signal overlap of multiple inputs with varioussignal frequencies.

FIGS. 22A-22C illustrate various arrays of electromagnetic sensingmodules according to some embodiments.

FIGS. 23A-23C illustrate recalibration of sensors with a given knowninput according to some embodiments.

FIGS. 24A-24D illustrate determining a variable in a calibrationprotocol according to some embodiments.

FIGS. 25A-25E illustrate potential false readings given certain sensorinputs and applied solutions according to some embodiments.

FIG. 25F illustrates a flowchart describing determining a correct poseof a hand-held controller using a position vector for the hand-helddevice relative to the headset according to some embodiments.

FIG. 26 illustrates feature matching as between two images according tosome embodiments.

FIGS. 27A-27B depict methods of determining pose given sensor inputaccording to some embodiments.

FIGS. 28A-28G illustrates various sensor fusion corrections according tosome embodiments.

FIG. 29 illustrates a single pathway multiple layer convolutionalcomputing architecture according to some embodiments.

FIGS. 30A-30E illustrate various coil configurations for anelectromagnetic tracking system according to some embodiments.

FIG. 31 illustrates a simplified computer system according to anembodiment described herein.

DETAILED DESCRIPTION

For example, referring to FIG. 1, an augmented reality scene (4) isdepicted wherein a user of an AR technology sees a real-world park-likesetting (6) featuring people, trees, buildings in the background, and aconcrete platform (1120). In addition to these items, the user of the ARtechnology also perceives that he “sees” a robot statue (1110) standingupon the real-world platform (1120), and a cartoon-like avatar character(2) flying by which seems to be a personification of a bumble bee, eventhough these elements (2, 1110) do not exist in the real world. As itturns out, the human visual perception system is very complex, andproducing a VR or AR technology that facilitates a comfortable,natural-feeling, rich presentation of virtual image elements amongstother virtual or real-world imagery elements is challenging.

For instance, head-worn AR displays (or helmet-mounted displays, orsmart glasses) typically are at least loosely coupled to a user's head,and thus move when the user's head moves. If the user's head motions aredetected by the display system, the data being displayed can be updatedto take the change in head pose into account.

As an example, if a user wearing a head-worn display views a virtualrepresentation of a three-dimensional (3D) object on the display andwalks around the area where the 3D object appears, that 3D object can bere-rendered for each viewpoint, giving the user the perception that heor she is walking around an object that occupies real space. If thehead-worn display is used to present multiple objects within a virtualspace (for instance, a rich virtual world), measurements of head pose(i.e., the location and orientation of the user's head) can be used tore-render the scene to match the user's dynamically changing headlocation and orientation and provide an increased sense of immersion inthe virtual space.

In AR systems, detection or calculation of head pose can facilitate thedisplay system to render virtual objects such that they appear to occupya space in the real world in a manner that makes sense to the user. Inaddition, detection of the position and/or orientation of a real object,such as handheld device (which also may be referred to as a “totem”),haptic device, or other real physical object, in relation to the user'shead or AR system may also facilitate the display system in presentingdisplay information to the user to enable the user to interact withcertain aspects of the AR system efficiently. As the user's head movesaround in the real world, the virtual objects may be re-rendered as afunction of head pose, such that the virtual objects appear to remainstable relative to the real world. At least for AR applications,placement of virtual objects in spatial relation to physical objects(e.g., presented to appear spatially proximate a physical object in two-or three-dimensions) may be a non-trivial problem.

For example, head movement may significantly complicate placement ofvirtual objects in a view of an ambient environment. Such is truewhether the view is captured as an image of the ambient environment andthen projected or displayed to the end user, or whether the end userperceives the view of the ambient environment directly. For instance,head movement will likely cause a field of view of the end user tochange, which will likely require an update to where various virtualobjects are displayed in the field of the view of the end user.

Additionally, head movements may occur within a large variety of rangesand speeds. Head movement speed may vary not only between different headmovements, but within or across the range of a single head movement. Forinstance, head movement speed may initially increase (e.g., linearly ornot) from a starting point, and may decrease as an ending point isreached, obtaining a maximum speed somewhere between the starting andending points of the head movement. Rapid head movements may even exceedthe ability of the particular display or projection technology to renderimages that appear uniform and/or as smooth motion to the end user.

Head tracking accuracy and latency (i.e., the elapsed time between whenthe user moves his or her head and the time when the image gets updatedand displayed to the user) have been challenges for VR and AR systems.Especially for display systems that fill a substantial portion of theuser's visual field with virtual elements, it is critical that theaccuracy of head-tracking is high and that the overall system latency isvery low from the first detection of head motion to the updating of thelight that is delivered by the display to the user's visual system. Ifthe latency is high, the system can create a mismatch between the user'svestibular and visual sensory systems, and generate a user perceptionscenario that can lead to motion sickness or simulator sickness. If thesystem latency is high, the apparent location of virtual objects willappear unstable during rapid head motions.

In addition to head-worn display systems, other display systems canbenefit from accurate and low latency head pose detection. These includehead-tracked display systems in which the display is not worn on theuser's body, but is, e.g., mounted on a wall or other surface. Thehead-tracked display acts like a window onto a scene, and as a usermoves his head relative to the “window” the scene is re-rendered tomatch the user's changing viewpoint. Other systems include a head-wornprojection system, in which a head-worn display projects light onto thereal world.

Additionally, in order to provide a realistic augmented realityexperience, AR systems may be designed to be interactive with the user.For example, multiple users may play a ball game with a virtual balland/or other virtual objects. One user may “catch” the virtual ball, andthrow the ball back to another user. In another embodiment, a first usermay be provided with a totem (e.g., a real bat communicatively coupledto the AR system) to hit the virtual ball. In other embodiments, avirtual user interface may be presented to the AR user to allow the userto select one of many options. The user may use totems, haptic devices,wearable components, or simply touch the virtual screen to interact withthe system.

Detecting head pose and orientation of the user, and detecting aphysical location of real objects in space enable the AR system todisplay virtual content in an effective and enjoyable manner. However,although these capabilities are key to an AR system, they are difficultto achieve. In other words, the AR system must recognize a physicallocation of a real object (e.g., user's head, totem, haptic device,wearable component, user's hand, etc.) and correlate the physicalcoordinates of the real object to virtual coordinates corresponding toone or more virtual objects being displayed to the user. This requireshighly accurate sensors and sensor recognition systems that track aposition and orientation of one or more objects at rapid rates. Currentapproaches do not perform localization at satisfactory speed orprecision standards.

Referring to FIGS. 2A-2D, some general componentry options areillustrated. In the portions of the detailed description which followthe discussion of FIGS. 2A-2D, various systems, subsystems, andcomponents are presented for addressing the objectives of providing ahigh-quality, comfortably-perceived display system for human VR and/orAR.

As shown in FIG. 2A, an AR system user (60) is depicted wearing headmounted component (58) featuring a frame (64) structure coupled to adisplay system (62) positioned in front of the eyes of the user. Aspeaker (66) is coupled to the frame (64) in the depicted configurationand positioned adjacent the ear canal of the user (in one embodiment,another speaker, not shown, is positioned adjacent the other ear canalof the user to provide for stereo/shapeable sound control). The display(62) is operatively coupled (68), such as by a wired lead or wirelessconnectivity, to a local processing and data module (70) which may bemounted in a variety of configurations, such as fixedly attached to theframe (64), fixedly attached to a helmet or hat (80) as shown in theembodiment of FIG. 2B, embedded in headphones, removably attached to thetorso (82) of the user (60) in a backpack-style configuration as shownin the embodiment of FIG. 2C, or removably attached to the hip (84) ofthe user (60) in a belt-coupling style configuration as shown in theembodiment of FIG. 2D.

The local processing and data module (70) may comprise a power-efficientprocessor or controller, as well as digital memory, such as flashmemory, both of which may be utilized to assist in the processing,caching, and storage of data a) captured from sensors which may beoperatively coupled to the frame (64), such as image capture devices(such as cameras), microphones, inertial measurement units,accelerometers, compasses, GPS units, radio devices, and/or gyros;and/or b) acquired and/or processed using the remote processing module(72) and/or remote data repository (74), possibly for passage to thedisplay (62) after such processing or retrieval.

The local processing and data module (70) may be operatively coupled(76, 78), such as via a wired or wireless communication links, to theremote processing module (72) and remote data repository (74) such thatthese remote modules (72, 74) are operatively coupled to each other andavailable as resources to the local processing and data module (70).

In one embodiment, the remote processing module (72) may comprise one ormore relatively powerful processors or controllers configured to analyzeand process data and/or image information. In one embodiment, the remotedata repository (74) may comprise a relatively large-scale digital datastorage facility, which may be available through the internet or othernetworking configuration in a “cloud” resource configuration. In oneembodiment, all data is stored and all computation is performed in thelocal processing and data module, allowing fully autonomous use from anyremote modules.

Referring now to FIG. 3, a schematic illustrates coordination betweenthe cloud computing assets (46) and local processing assets, which may,for example reside in head mounted componentry (58) coupled to theuser's head (120) and a local processing and data module (70), coupledto the user's belt (308; therefore the component 70 may also be termed a“belt pack” 70), as shown in FIG. 3. In one embodiment, the cloud (46)assets, such as one or more server systems (110) are operatively coupled(115), such as via wired or wireless networking (wireless beingpreferred for mobility, wired being preferred for certain high-bandwidthor high-data-volume transfers that may be desired), directly to (40, 42)one or both of the local computing assets, such as processor and memoryconfigurations, coupled to the user's head (120) and belt (308) asdescribed above. These computing assets local to the user may beoperatively coupled to each other as well, via wired and/or wirelessconnectivity configurations (44), such as the wired coupling (68)discussed below in reference to FIG. 8. In one embodiment, to maintain alow-inertia and small-size subsystem mounted to the user's head (120),primary transfer between the user and the cloud (46) may be via the linkbetween the subsystem mounted at the belt (308) and the cloud, with thehead mounted (120) subsystem primarily data-tethered to the belt-based(308) subsystem using wireless connectivity, such as ultra-wideband(“UWB”) connectivity, as is currently employed, for example, in personalcomputing peripheral connectivity applications.

With efficient local and remote processing coordination, and anappropriate display device for a user, such as the user interface oruser display system (62) shown in FIG. 2A, or variations thereof,aspects of one world pertinent to a user's current actual or virtuallocation may be transferred or “passed” to the user and updated in anefficient fashion. In other words, a map of the world may be continuallyupdated at a storage location which may partially reside on the user'sAR system and partially reside in the cloud resources. The map (alsoreferred to as a “passable world model”) may be a large databasecomprising raster imagery, 3-D and 2-D points, parametric informationand other information about the real world. As more and more AR userscontinually capture information about their real environment (e.g.,through cameras, sensors, IMUS, etc.), the map becomes more and moreaccurate and complete.

With a configuration as described above, wherein there is one worldmodel that can reside on cloud computing resources and be distributedfrom there, such world can be “passable” to one or more users in arelatively low bandwidth form preferable to trying to pass aroundreal-time video data or the like. The augmented experience of the personstanding near the statue (i.e., as shown in FIG. 1) may be informed bythe cloud-based world model, a subset of which may be passed down tothem and their local display device to complete the view. A personsitting at a remote display device, which may be as simple as a personalcomputer sitting on a desk, can efficiently download that same sectionof information from the cloud and have it rendered on their display.Indeed, one person actually present in the park near the statue may takea remotely-located friend for a walk in that park, with the friendjoining through virtual and augmented reality. The system will need toknow where the street is, wherein the trees are, where the statue is—butwith that information on the cloud, the joining friend can download fromthe cloud aspects of the scenario, and then start walking along as anaugmented reality local relative to the person who is actually in thepark.

3-D points may be captured from the environment, and the pose (i.e.,vector and/or origin position information relative to the world) of thecameras that capture those images or points may be determined, so thatthese points or images may be “tagged”, or associated, with this poseinformation. Then points captured by a second camera may be utilized todetermine the pose of the second camera. In other words, one can orientand/or localize a second camera based upon comparisons with taggedimages from a first camera. Then this knowledge may be utilized toextract textures, make maps, and create a virtual copy of the real world(because then there are two cameras around that are registered).

So at the base level, in one embodiment a person-worn system can beutilized to capture both 3-D points and the 2-D images that produced thepoints, and these points and images may be sent out to a cloud storageand processing resource. They may also be cached locally with embeddedpose information (i.e., cache the tagged images); so the cloud may haveon the ready (i.e., in available cache) tagged 2-D images (i.e., taggedwith a 3-D pose), along with 3-D points. If a user is observingsomething dynamic, he may also send additional information up to thecloud pertinent to the motion (for example, if looking at anotherperson's face, the user can take a texture map of the face and push thatup at an optimized frequency even though the surrounding world isotherwise basically static). More information on object recognizers andthe passable world model may be found in U.S. patent application Ser.No. 14/205,126, entitled “System and method for augmented and virtualreality”, which is incorporated by reference in its entirety herein,along with the following additional disclosures, which related toaugmented and virtual reality systems such as those developed by MagicLeap, Inc. of Fort Lauderdale, Fla.; U.S. patent application Ser. No.14/641,376; U.S. patent application Ser. No. 14/555,585; U.S. patentapplication Ser. No. 14/212,961; U.S. patent application Ser. No.14/690,401; U.S. patent application Ser. No. 13/663,466; patentapplication Ser. No. 13/684,489 and U.S. GPS and other localizationinformation may be utilized as inputs to such processing. Highlyaccurate localization of the user's head, totems, hand gestures, hapticdevices etc. are crucial in displaying appropriate virtual content tothe user.

One approach to achieve high precision localization may involve the useof an electromagnetic field coupled with electromagnetic sensors thatare strategically placed on the user's AR head set, belt pack, and/orother ancillary devices (e.g., totems, haptic devices, gaminginstruments, etc.).

Electromagnetic tracking systems typically comprise at least anelectromagnetic field emitter and at least one electromagnetic fieldsensor. The sensors may measure electromagnetic fields with a knowndistribution. Based on these measurements a position and orientation ofa field sensor relative to the emitter is determined.

Referring now to FIG. 4, an example system diagram of an electromagnetictracking system (e.g., such as those developed by organizations such asthe Biosense® division of Johnson & Johnson Corporation, Polhemus®, Inc.of Colchester, Vt., manufactured by Sixense® Entertainment, Inc. of LosGatos, Calif., and other tracking companies) is illustrated. In one ormore embodiments, the electromagnetic tracking system comprises anelectromagnetic field emitter 402 which is configured to emit a knownmagnetic field. As shown in FIG. 4, the electromagnetic field emittermay be coupled to a power supply (e.g., electric current, batteries,etc.) to provide power to the emitter 402.

In one or more embodiments, the electromagnetic field emitter 402comprises several coils (e.g., at least three coils positionedperpendicular to each other to produce field in the x, y and zdirections) that generate magnetic fields. This magnetic field is usedto establish a coordinate space. This allows the system to map aposition of the sensors in relation to the known magnetic field, andhelps determine a position and/or orientation of the sensors. In one ormore embodiments, the electromagnetic sensors 404 a, 404 b, etc. may beattached to one or more real objects. The electromagnetic sensors 404may comprise smaller coils in which current may be induced through theemitted electromagnetic field.

Generally the “sensor” components (404) may comprise small coils orloops, such as a set of three differently-oriented (i.e., such asorthogonally oriented relative to each other) coils coupled togetherwithin a small structure such as a cube or other container, that arepositioned/oriented to capture incoming magnetic flux from the magneticfield emitted by the emitter (402), and by comparing currents inducedthrough these coils, and knowing the relative positioning andorientation of the coils relative to each other, relative position andorientation of a sensor relative to the emitter may be calculated.

One or more parameters pertaining to a behavior of the coils andinertial measurement unit (“IMU”) components operatively coupled to theelectromagnetic tracking sensors may be measured to detect a positionand/or orientation of the sensor (and the object to which it is attachedto) relative to a coordinate system to which the electromagnetic fieldemitter is coupled. In one or more embodiments, multiple sensors may beused in relation to the electromagnetic emitter to detect a position andorientation of each of the sensors within the coordinate space. Theelectromagnetic tracking system may provide positions in threedirections (i.e., X, Y and Z directions), and further in two or threeorientation angles. In one or more embodiments, measurements of the IMUmay be compared to the measurements of the coil to determine a positionand orientation of the sensors. In one or more embodiments, bothelectromagnetic (EM) data and IMU data, along with various other sourcesof data, such as cameras, depth sensors, and other sensors, may becombined to determine the position and orientation. This information maybe transmitted (e.g., wireless communication, Bluetooth, etc.) to thecontroller 406. In one or more embodiments, pose (or position andorientation) may be reported at a relatively high refresh rate inconventional systems.

Conventionally an electromagnetic emitter is coupled to a relativelystable and large object, such as a table, operating table, wall, orceiling, and one or more sensors are coupled to smaller objects, such asmedical devices, handheld gaming components, or the like. Alternatively,as described below in reference to FIG. 6, various features of theelectromagnetic tracking system may be employed to produce aconfiguration wherein changes or deltas in position and/or orientationbetween two objects that move in space relative to a more stable globalcoordinate system may be tracked; in other words, a configuration isshown in FIG. 6 wherein a variation of an electromagnetic trackingsystem may be utilized to track position and orientation delta between ahead-mounted component and a hand-held component, while head poserelative to the global coordinate system (say of the room environmentlocal to the user) is determined otherwise, such as by simultaneouslocalization and mapping (“SLAM”) techniques using outward-capturingcameras which may be coupled to the head mounted component of thesystem.

The controller 406 may control the electromagnetic field generator 402,and may also capture data from the various electromagnetic sensors 404.It should be appreciated that the various components of the system maybe coupled to each other through any electro-mechanical orwireless/Bluetooth means. The controller 406 may also comprise dataregarding the known magnetic field, and the coordinate space in relationto the magnetic field. This information is then used to detect theposition and orientation of the sensors in relation to the coordinatespace corresponding to the known electromagnetic field.

One advantage of electromagnetic tracking systems is that they producehighly accurate tracking results with minimal latency and highresolution. Additionally, the electromagnetic tracking system does notnecessarily rely on optical trackers, and sensors/objects not in theuser's line-of-vision may be easily tracked.

It should be appreciated that the strength of the electromagnetic fieldv drops as a cubic function of distance r from a coil transmitter (e.g.,electromagnetic field emitter 402). Thus, an algorithm may be requiredbased on a distance away from the electromagnetic field emitter. Thecontroller 406 may be configured with such algorithms to determine aposition and orientation of the sensor/object at varying distances awayfrom the electromagnetic field emitter.

Given the rapid decline of the strength of the electromagnetic field asone moves farther away from the electromagnetic emitter, best results,in terms of accuracy, efficiency and low latency, may be achieved atcloser distances. In typical electromagnetic tracking systems, theelectromagnetic field emitter is powered by electric current (e.g.,plug-in power supply) and has sensors located within 20 ft radius awayfrom the electromagnetic field emitter. A shorter radius between thesensors and field emitter may be more desirable in many applications,including AR applications.

Referring now to FIG. 5, an example flowchart describing a functioningof a typical electromagnetic tracking system is briefly described. At502, a known electromagnetic field is emitted. In one or moreembodiments, the magnetic field emitter may generate magnetic fieldseach coil may generate an electric field in one direction (e.g., x, y orz). The magnetic fields may be generated with an arbitrary waveform.

In one or more embodiments, each of the axes may oscillate at a slightlydifferent frequency. At 504, a coordinate space corresponding to theelectromagnetic field may be determined. For example, the control 406 ofFIG. 4 may automatically determine a coordinate space around the emitterbased on the electromagnetic field.

At 506, a behavior of the coils at the sensors (which may be attached toa known object) may be detected. For example, a current induced at thecoils may be calculated. In other embodiments, a rotation of coils, orany other quantifiable behavior may be tracked and measured. At 508,this behavior may be used to detect a position and orientation of thesensor(s) and/or known object. For example, the controller 406 mayconsult a mapping table that correlates a behavior of the coils at thesensors to various positions or orientations. Based on thesecalculations, the position in the coordinate space along with theorientation of the sensors may be determined.

In the context of AR systems, one or more components of theelectromagnetic tracking system may need to be modified to facilitateaccurate tracking of mobile components. As described above, tracking theuser's head pose and orientation is crucial in many AR applications.Accurate determination of the user's head pose and orientation allowsthe AR system to display the right virtual content to the user. Forexample, the virtual scene may comprise a monster hiding behind a realbuilding. Depending on the pose and orientation of the user's head inrelation to the building, the view of the virtual monster may need to bemodified such that a realistic AR experience is provided. Or, a positionand/or orientation of a totem, haptic device or some other means ofinteracting with a virtual content may be important in enabling the ARuser to interact with the AR system. For example, in many gamingapplications, the AR system must detect a position and orientation of areal object in relation to virtual content. Or, when displaying avirtual interface, a position of a totem, user's hand, haptic device orany other real object configured for interaction with the AR system mustbe known in relation to the displayed virtual interface in order for thesystem to understand a command, etc. Conventional localization methodsincluding optical tracking and other methods are typically plagued withhigh latency and low resolution problems, which makes rendering virtualcontent challenging in many augmented reality applications.

In one or more embodiments, the electromagnetic tracking system,discussed in relation to FIGS. 4 and 5 may be adapted to the AR systemto detect position and orientation of one or more objects in relation toan emitted electromagnetic field.

Typical electromagnetic systems tend to have a large and bulkyelectromagnetic emitters (e.g., 402 in FIG. 4), which is problematic forAR devices. However, smaller electromagnetic emitters (e.g., in themillimeter range) may be used to emit a known electromagnetic field inthe context of the AR system.

Referring now to FIG. 6, an electromagnetic tracking system may beincorporated with an AR system as shown, with an electromagnetic fieldemitter 602 incorporated as part of a hand-held controller 606. In oneor more embodiments, the hand-held controller may be a totem to be usedin a gaming scenario. In other embodiments, the hand-held controller maybe a haptic device. In yet other embodiments, the electromagnetic fieldemitter may simply be incorporated as part of the belt pack 70. Thehand-held controller 606 may comprise a battery 610 or other powersupply that powers that electromagnetic field emitter 602. It should beappreciated that the electromagnetic field emitter 602 may also compriseor be coupled to an IMU 650 component configured to assist indetermining positioning and/or orientation of the electromagnetic fieldemitter 602 relative to other components. This may be especiallyimportant in cases where both the field emitter 602 and the sensors(604) are mobile. Placing the electromagnetic field emitter 602 in thehand-held controller rather than the belt pack, as shown in theembodiment of FIG. 6, ensures that the electromagnetic field emitter isnot competing for resources at the belt pack, but rather uses its ownbattery source at the hand-held controller 606.

In one or more embodiments, the electromagnetic sensors 604 may beplaced on one or more locations on the user's headset, along with othersensing devices such as one or more IMUs or additional magnetic fluxcapturing coils 608. For example, as shown in FIG. 6, sensors (604, 608)may be placed on either side of the head set (58). Since these sensorsare engineered to be rather small (and hence may be less sensitive, insome cases), having multiple sensors may improve efficiency andprecision.

In one or more embodiments, one or more sensors may also be placed onthe belt pack 70 or any other part of the user's body. The sensors (604,608) may communicate wirelessly or through Bluetooth to a computingapparatus that determines a pose and orientation of the sensors (and theAR headset to which it is attached). In one or more embodiments, thecomputing apparatus may reside at the belt pack 70. In otherembodiments, the computing apparatus may reside at the headset itself,or even the hand-held controller 606. The computing apparatus may inturn comprise a mapping database (e.g., passable world model, coordinatespace, etc.) to detect pose, to determine the coordinates of realobjects and virtual objects, and may even connect to cloud resources andthe passable world model, in one or more embodiments.

As described above, conventional electromagnetic emitters may be toobulky for AR devices. Therefore the electromagnetic field emitter may beengineered to be compact, using smaller coils compared to traditionalsystems. However, given that the strength of the electromagnetic fielddecreases as a cubic function of the distance away from the fieldemitter, a shorter radius between the electromagnetic sensors 604 andthe electromagnetic field emitter 602 (e.g., about 3-3.5 ft) may reducepower consumption when compared to conventional systems such as the onedetailed in FIG. 4.

This aspect may either be utilized to prolong the life of the battery610 that may power the controller 606 and the electromagnetic fieldemitter 602, in one or more embodiments. Or, in other embodiments, thisaspect may be utilized to reduce the size of the coils generating themagnetic field at the electromagnetic field emitter 602. However, inorder to get the same strength of magnetic field, the power may be needto be increased. This allows for a compact electromagnetic field emitterunit 602 that may fit compactly at the hand-held controller 606.

Several other changes may be made when using the electromagnetictracking system for AR devices. Although this pose reporting rate israther good, AR systems may require an even more efficient posereporting rate. To this end, IMU-based pose tracking may be used in thesensors. Crucially, the IMUs must remain as stable as possible in orderto increase an efficiency of the pose detection process. The IMUs may beengineered such that they remain stable up to 50-100 milliseconds. Itshould be appreciated that some embodiments may utilize an outside poseestimator module (i.e., IMUs may drift over time) that may enable poseupdates to be reported at a rate of 10-20 Hz. By keeping the IMUs stableat a reasonable rate, the rate of pose updates may be dramaticallydecreased to 10-20 Hz (as compared to higher frequencies in conventionalsystems).

If the electromagnetic tracking system can be run at a 10% duty cycle(e.g., only pinging for ground truth every 100 milliseconds), this wouldbe another way to save power at the AR system. This would mean that theelectromagnetic tracking system wakes up every 10 milliseconds out ofevery 100 milliseconds to generate a pose estimate. This directlytranslates to power consumption savings, which may, in turn, affectsize, battery life and cost of the AR device.

In one or more embodiments, this reduction in duty cycle may bestrategically utilized by providing two hand-held controllers (notshown) rather than just one. For example, the user may be playing a gamethat requires two totems, etc. Or, in a multi-user game, two users mayhave their own totems/hand-held controllers to play the game. When twocontrollers (e.g., symmetrical controllers for each hand) are usedrather than one, the controllers may operate at offset duty cycles. Thesame concept may also be applied to controllers utilized by twodifferent users playing a multi-player game, for example.

Referring now to FIG. 7, an example flow chart describing theelectromagnetic tracking system in the context of AR devices isdescribed. At 702, the hand-held controller emits a magnetic field. At704, the electromagnetic sensors (placed on headset, belt pack, etc.)detect the magnetic field. At 706, a position and orientation of theheadset/belt is determined based on a behavior of the coils/IMUs at thesensors. At 708, the pose information is conveyed to the computingapparatus (e.g., at the belt pack or headset). At 710, optionally, amapping database (e.g., passable world model) may be consulted tocorrelate the real world coordinates with the virtual world coordinates.At 712, virtual content may be delivered to the user at the AR headset.It should be appreciated that the flowchart described above is forillustrative purposes only, and should not be read as limiting.

Advantageously, using an electromagnetic tracking system similar to theone outlined in FIG. 6 enables pose tracking (e.g., head position andorientation, position and orientation of totems, and other controllers).This allows the AR system to project virtual content with a higherdegree of accuracy, and very low latency when compared to opticaltracking techniques.

Referring to FIG. 8, a system configuration is illustrated whereinfeaturing many sensing components. A head mounted wearable component(58) is shown operatively coupled (68) to a local processing and datamodule (70), such as a belt pack, here using a physical multicore leadwhich also features a control and quick release module (86) as describedbelow in reference to FIGS. 9A-9F. The local processing and data module(70) is operatively coupled (100) to a hand held component (606), hereby a wireless connection such as low power Bluetooth; the hand heldcomponent (606) may also be operatively coupled (94) directly to thehead mounted wearable component (58), such as by a wireless connectionsuch as low power Bluetooth. Generally where IMU data is passed tocoordinate pose detection of various components, a high-frequencyconnection is desirable, such as in the range of hundreds or thousandsof cycles/second or higher; tens of cycles per second may be adequatefor electromagnetic localization sensing, such as by the sensor (604)and transmitter (602) pairings. Also shown is a global coordinate system(10), representative of fixed objects in the real world around the user,such as a wall (8). Cloud resources (46) also may be operatively coupled(42, 40, 88, 90) to the local processing and data module (70), to thehead mounted wearable component (58), to resources which may be coupledto the wall (8) or other item fixed relative to the global coordinatesystem (10), respectively. The resources coupled to the wall (8) orhaving known positions and/or orientations relative to the globalcoordinate system (10) may include a WiFi transceiver (114), anelectromagnetic emitter (602) and/or receiver (604), a beacon orreflector (112) configured to emit or reflect a given type of radiation,such as an infrared LED beacon, a cellular network transceiver (110), aRADAR emitter or detector (108), a LIDAR emitter or detector (106), aGPS transceiver (118), a poster or marker having a known detectablepattern (122), and a camera (124). The head mounted wearable component(58) features similar components, as illustrated, in addition tolighting emitters (130) configured to assist the camera (124) detectors,such as infrared emitters (130) for an infrared camera (124); alsofeatured on the head mounted wearable component (58) are one or morestrain gauges (116), which may be fixedly coupled to the frame ormechanical platform of the head mounted wearable component (58) andconfigured to determine deflection of such platform in betweencomponents such as electromagnetic receiver sensors (604) or displayelements (62), wherein it may be valuable to understand if bending ofthe platform has occurred, such as at a thinned portion of the platform,such as the portion above the nose on the eyeglasses-like platformdepicted in FIG. 8. The head mounted wearable component (58) alsofeatures a processor (128) and one or more IMUs (102). Each of thecomponents preferably are operatively coupled to the processor (128).The hand held component (606) and local processing and data module (70)are illustrated featuring similar components. As shown in FIG. 8, withso many sensing and connectivity means, such a system is likely to beheavy, power hungry, large, and relatively expensive. However, forillustrative purposes, such a system may be utilized to provide a veryhigh level of connectivity, system component integration, andposition/orientation tracking. For example, with such a configuration,the various main mobile components (58, 70, 606) may be localized interms of position relative to the global coordinate system using WiFi,GPS, or Cellular signal triangulation; beacons, electromagnetic tracking(as described above), RADAR, and LIDAR systems may provide yet furtherlocation and/or orientation information and feedback. Markers andcameras also may be utilized to provide further information regardingrelative and absolute position and orientation. For example, the variouscamera components (124), such as those shown coupled to the head mountedwearable component (58), may be utilized to capture data which may beutilized in simultaneous localization and mapping protocols, or “SLAM”,to determine where the component (58) is and how it is oriented relativeto other components.

Referring to FIGS. 9A-9F, various aspects of the control and quickrelease module (86) are depicted. Referring to FIG. 9A, two outerhousing components are coupled together using a magnetic couplingconfiguration which may be enhanced with mechanical latching. Buttons(136) for operation of the associated system may be included. FIG. 9Billustrates a partial cutaway view with the buttons (136) and underlyingtop printed circuit board (138) shown. Referring to FIG. 9C, with thebuttons (136) and underlying top printed circuit board (138) removed, afemale contact pin array (140) is visible. Referring to FIG. 9D, with anopposite portion of housing (134) removed, the lower printed circuitboard (142) is visible. With the lower printed circuit board (142)removed, as shown in FIG. 9E, a male contact pin array (144) is visible.Referring to the cross-sectional view of FIG. 9F, at least one of themale pins or female pins are configured to be spring-loaded such thatthey may be depressed along each pin's longitudinal axis; the pins maybe termed “pogo pins” and generally comprise a highly conductivematerial, such as copper or gold. When assembled, the illustratedconfiguration mates 46 male pins with female pins, and the entireassembly may be quick-release decoupled in half by manually pulling itapart and overcoming a magnetic interface (146) load which may bedeveloped using north and south magnets oriented around the perimetersof the pin arrays (140, 144). In one embodiment, an approximate 2 kgload from compressing the 46 pogo pins is countered with a closuremaintenance force of about 4 kg. The pins in the array may be separatedby about 1.3 mm, and the pins may be operatively coupled to conductivelines of various types, such as twisted pairs or other combinations tosupport USB 3.0, HDMI 2.0, I2S signals, GPIO, and MIPI configurations,and high current analog lines and grounds configured for up to about 4amps/5 volts in one embodiment.

Referring to FIG. 10, it is helpful to have a minimizedcomponent/feature set to be able to minimize the weight and bulk of thevarious components, and to arrive at a relatively slim head mountedcomponent, for example, such as that (58) featured in FIG. 10. Thusvarious permutations and combinations of the various components shown inFIG. 8 may be utilized.

Referring to FIG. 11A, an electromagnetic sensing coil assembly (604,i.e., 3 individual coils coupled to a housing) is shown coupled to ahead mounted component (58); such a configuration adds additionalgeometry to the overall assembly which may not be desirable. Referringto FIG. 11B, rather than housing the coils in a box or single housing asin the configuration of FIG. 11A, the individual coils may be integratedinto the various structures of the head mounted component (58), as shownin FIG. 11B. FIGS. 12A-12E illustrate various configurations forfeaturing a ferrite core coupled to an electromagnetic sensor toincrease field sensitivity; the embodiments of FIGS. 12B-12E are lighterin weight than the solid core configuration of FIG. 12A and may beutilized to save mass.

Referring to FIGS. 13A-13C, time division multiplexing (“TDM”) may beutilized to save mass as well. For example, referring to FIG. 13A, aconventional local data processing configuration is shown for a 3-coilelectromagnetic receiver sensor, wherein analog currents come in fromeach of the X, Y, and Z coils, go into a pre-amplifier, go into a bandpass filter, through analog-to-digital conversion, and ultimately to adigital signal processor. Referring to the transmitter configuration ofFIG. 13B, and the receiver configuration of FIG. 13C, time divisionmultiplexing may be utilized to share hardware, such that each coilsensor chain doesn't require its own amplifiers, etc. In addition toremoving sensor housings, and multiplexing to save on hardware overhead,signal to noise ratios may be increased by having more than one set ofelectromagnetic sensors, each set being relatively small relative to asingle larger coil set; also the low-side frequency limits, whichgenerally are needed to have multiple sensing coils in close proximity,may be improved to facilitate bandwidth requirement improvements. Also,there is a tradeoff with multiplexing, in that multiplexing generallyspreads out the reception of radiofrequency signals in time, whichresults in generally dirtier signals; thus larger coil diameter may berequired for multiplexed systems. For example, where a multiplexedsystem may require a 9 mm-side dimension cubic coil sensor box, anon-multiplexed system may only require a 7 mm-side dimension cubic coilbox for similar performance; thus there are tradeoffs in minimizinggeometry and mass.

In another embodiment wherein a particular system component, such as ahead mounted component (58) features two or more electromagnetic coilsensor sets, the system may be configured to selectively utilize thesensor and emitter pairing that are closest to each other to optimizethe performance of the system.

Referring to FIG. 14, in one embodiment, after a user powers up his orher wearable computing system (160), a head mounted component assemblymay capture a combination of IMU and camera data (the camera data beingused, for example, for SLAM analysis, such as at the belt pack processorwhere there may be more raw processing horsepower present) to determineand update head pose (i.e., position and orientation) relative to a realworld global coordinate system (162). The user may also activate ahandheld component to, for example, play an augmented reality game(164), and the handheld component may comprise an electromagnetictransmitter operatively coupled to one or both of the belt pack and headmounted component (166). One or more electromagnetic field coil receiversets (i.e., a set being 3 differently-oriented individual coils) coupledto the head mounted component to capture magnetic flux from thetransmitter, which may be utilized to determine positional ororientational difference (or “delta”), between the head mountedcomponent and handheld component (168). The combination of the headmounted component assisting in determining pose relative to the globalcoordinate system, and the hand held assisting in determining relativelocation and orientation of the handheld relative to the head mountedcomponent, allows the system to generally determine where each componentis relative to the global coordinate system, and thus the user's headpose, and handheld pose may be tracked, preferably at relatively lowlatency, for presentation of augmented reality image features andinteraction using movements and rotations of the handheld component(170).

Referring to FIG. 15, an embodiment is illustrated that is somewhatsimilar to that of FIG. 14, with the exception that the system has manymore sensing devices and configurations available to assist indetermining pose of both the head mounted component (172) and a handheld component (176, 178), such that the user's head pose, and handheldpose may be tracked, preferably at relatively low latency, forpresentation of augmented reality image features and interaction usingmovements and rotations of the handheld component (180).

Referring to FIGS. 16A and 16B, various aspects of a configurationsimilar to that of FIG. 8 are shown. The configuration of FIG. 16Adiffers from that of FIG. 8 in that in addition to a LIDAR (106) type ofdepth sensor, the configuration of FIG. 16A features a generic depthcamera or depth sensor (154) for illustrative purposes, which may, forexample, be either a stereo triangulation style depth sensor (such as apassive stereo depth sensor, a texture projection stereo depth sensor,or a structured light stereo depth sensor) or a time or flight styledepth sensor (such as a LIDAR depth sensor or a modulated emission depthsensor); further, the configuration of FIG. 16A has an additionalforward facing “world” camera (124, which may be a grayscale camera,having a sensor capable of 720p range resolution) as well as arelatively high-resolution “picture camera” (156, which may be a fullcolor camera, having a sensor capable of 2 megapixel or higherresolution, for example). FIG. 16B shows a partial orthogonal view ofthe configuration of FIG. 16A for illustrative purposes, as describedfurther below in reference to FIG. 16B.

Referring back to FIG. 16A and the stereo vs time-of-flight style depthsensors mentioned above, each of these depth sensor types may beemployed with a wearable computing solution as disclosed herein,although each has various advantages and disadvantages. For example,many depth sensors have challenges with black surfaces and shiny orreflective surfaces. Passive stereo depth sensing is a relativelysimplistic way of getting triangulation for calculating depth with adepth camera or sensor, but it may be challenged if a wide field of view(“FOV”) is required, and may require relatively significant computingresource; further, such a sensor type may have challenges with edgedetection, which may be important for the particular use case at hand.Passive stereo may have challenges with textureless walls, low lightsituations, and repeated patterns. Passive stereo depth sensors areavailable from manufacturers such as Intel® and Aquifi®. Stereo withtexture projection (also known as “active stereo”) is similar to passivestereo, but a texture projector broadcasts a projection pattern onto theenvironment, and the more texture that is broadcasted, the more accuracyis available in triangulating for depth calculation. Active stereo mayalso require relatively high compute resource, present challenges whenwide FOV is required, and be somewhat suboptimal in detecting edges, butit does address some of the challenges of passive stereo in that it iseffective with textureless walls, is good in low light, and generallydoes not have problems with repeating patterns.

Active stereo depth sensors are available from manufacturers such asIntel® and Aquifi®. Stereo with structured light, such as the systemsdeveloped by Primesense, Inc.® and available under the tradenameKinect®, as well as the systems available from Mantis Vision, Inc.®,generally utilize a single camera/projector pairing, and the projectoris specialized in that it is configured to broadcast a pattern of dotsthat is known apriori. In essence, the system knows the pattern that isbroadcasted, and it knows that the variable to be determined is depth.Such configurations may be relatively efficient on compute load, and maybe challenged in wide FOV requirement scenarios as well as scenarioswith ambient light and patterns broadcasted from other nearby devices,but can be quite effective and efficient in many scenarios. Withmodulated time of flight type depth sensors, such as those availablefrom PMD Technologies®, A.G. and SoftKinetic Inc.®, an emitter may beconfigured to send out a wave, such as a sine wave, of amplitudemodulated light; a camera component, which may be positioned nearby oreven overlapping in some configurations, receives a returning signal oneach of the pixels of the camera component and depth mapping may bedetermined/calculated. Such configurations may be relatively compact ingeometry, high in accuracy, and low in compute load, but may bechallenged in terms of image resolution (such as at edges of objects),multi-path errors (such as wherein the sensor is aimed at a reflectiveor shiny corner and the detector ends up receiving more than one returnpath, such that there is some depth detection aliasing. Direct time offlight sensors, which also may be referred to as the aforementionedLIDAR, are available from suppliers such as LuminAR® and AdvancedScientific Concepts, Inc.®. With these time of flight configurations,generally a pulse of light (such as a picosecond, nanosecond, orfemtosecond long pulse of light) is sent out to bathe the world orientedaround it with this light ping; then each pixel on a camera sensor waitsfor that pulse to return, and knowing the speed of light, the distanceat each pixel may be calculated. Such configurations may have many ofthe advantages of modulated time of flight sensor configurations (nobaseline, relatively wide FOV, high accuracy, relatively low computeload, etc.) and also relatively high framerates, such as into the tensof thousands of Hertz. They may also be relatively expensive, haverelatively low resolution, be sensitive to bright light, and susceptibleto multi-path errors; they may also be relatively large and heavy.

Referring to FIG. 16, a partial top view is shown for illustrativepurposes featuring a user's eyes (12) as well as cameras (14, such asinfrared cameras) with fields of view (28, 30) and light or radiationsources (16, such as infrared) directed toward the eyes (12) tofacilitate eye tracking, observation, and/or image capture. The threeoutward-facing world-capturing cameras (124) are shown with their FOVs(18, 20, 22), as is the depth camera (154) and its FOV (24), and thepicture camera (156) and its FOV (26). The depth information garneredfrom the depth camera (154) may be bolstered by using the overlappingFOVs and data from the other forward-facing cameras. For example, thesystem may end up with something like a sub-VGA image from the depthsensor (154), a 720p image from the world cameras (124), andoccasionally a 2 megapixel color image from the picture camera (156).Such a configuration has 4 cameras sharing common FOV, two of them withheterogeneous visible spectrum images, one with color, and the third onewith relatively low-resolution depth. The system may be configured to doa segmentation in the grayscale and color images, fuse those two andmake a relatively high-resolution image from them, get some stereocorrespondences, use the depth sensor to provide hypotheses about stereodepth, and use stereo correspondences to get a more refined depth map,which may be significantly better than what was available from the depthsensor only. Such processes may be run on local mobile processinghardware, or can run using cloud computing resources, perhaps along withthe data from others in the area (such as two people sitting across atable from each other nearby), and end up with quite a refined mapping.

In another embodiment, all of the above sensors may be combined into oneintegrated sensor to accomplish such functionality.

Referring to FIGS. 17A-17G, aspects of a dynamic transmission coiltuning configuration are shown for electromagnetic tracking, tofacilitate the transmission coil to operate optimally at multiplefrequencies per orthogonal axis, which allows for multiple users tooperate on the same system. Typically an electromagnetic trackingtransmitter will be designed to operate at fixed frequencies perorthogonal axis. With such approach, each transmission coil is tunedwith a static series capacitance that creates resonance only at thefrequency of operation. Such resonance allows for the maximum possiblecurrent flow through the coil which, in turn, maximizes the magneticflux generated.

FIG. 17A illustrates a typical resonant circuit used to createresonance. Element “L1” represents a single axis transmission coil at 1mH, and with capacitance set to 52 nF, resonance is created at 22 kHz,as shown in FIG. 17B.

FIG. 17C shows the current through the system plotted versus frequency,and it may be seen that the current is maximum at the resonantfrequency. If this system is expected to operate any other frequency,the operating circuit will not be at the possible maximum. FIG. 17Dillustrates an embodiment of a dynamically tunable configuration. Thedynamic frequency tuning may be set to achieve resonance on the coil toget maximum current flow; an example of a tunable circuit is shown inFIG. 17E, where one capacitor (“C4”) may be tuned to produce simulateddata, as shown in FIG. 17F. As shown in FIG. 17F, one of the orthogonalcoils of an electromagnetic tracker is simulated as “L1” and a staticcapacitor (“C5”) is a fixed high voltage capacitor. This high voltagecapacitor will see the higher voltages due to the resonance, and so it'spackage size generally will be larger. C4 will be the capacitor which isdynamically switched with different values, and can thus see a lowermaximum voltage and generally be a smaller geometric package to saveplacement space. L3 can also be utilized to fine tune the resonantfrequency. FIG. 17F illustrates the resonance achieved with the higherplots (248) versus the lower plots (250); as C4 is varied in thesimulation, the resonance is changed, and it is notable that the voltageacross C5 (Vmid-Vout) is higher than that across C4 (Vout). Thisgenerally will allow for a smaller package part on C4 since multiples ofthis generally will be needed for the system, one per frequency ofoperation. FIG. 17G illustrates that the maximum current achievedfollows the resonance regardless of voltage across capacitors.

Referring to FIGS. 18A-18C, an electromagnetic tracking system may bebounded to work below about 30 KHz, which is slightly higher than theaudible range for human hearing. Referring to FIG. 18A, there may besome audio systems which create noise in the usable frequencies for suchelectromagnetic tracking systems. Further, audio speakers typically havemagnetic fields and one or more coils which also may interfere withelectromagnetic tracking systems.

Referring to FIG. 18B, a block diagram is shown for a noise cancellingconfiguration for electromagnetic tracking interference. Since theunintentional interference is a known entity, this knowledge can be usedto cancel the interference and improve performance. In other words, theaudio generated by the system may be utilized to eliminate the effectsreceived by the receiver coil. The noise cancelling circuit may beconfigured to accept the corrupted signals from the EM amplifier as wellas the signal from the audio system, and the noise cancelling systemwill cancel out the noise received from the audio speaker. FIG. 18Cillustrates a plot to show an example of the how the signal can beinverted and added to cancel the interferer. V(Vnoise), the top plot, isthe noise added to the system by the audio speaker. Referring to FIG.19, in one embodiment a known pattern (such as a circular pattern) oflights or other emitters may be utilized to assist in calibration ofvision systems. For example, the circular pattern may be utilized as afiducial; as a camera or other capture device with known orientationcaptures the shape of the pattern while the object coupled to thepattern is reoriented, the orientation of the object, such as a handheld totem device, may be determined; such orientation may be comparedwith that which comes from an associated IMU device for errordetermination and use in calibration.

Referring to FIGS. 20A-20C, a configuration is shown with a summingamplifier to simplify circuitry between two subsystems or components ofa wearable computing configuration such as a head mounted component andbelt-pack component. With a conventional configuration, each of thecoils (on the left of FIG. 20A) of an electromagnetic tracking sensor isassociated with an amplifier, and three distinct amplified signals wouldbe sent through the cabling to the other component. In the illustratedembodiment, the three distinct amplified signals may be directed to asumming amplifier, which produces one amplified signal that is directeddown an advantageously simplified cable, each signal at a differentfrequency. The summing amplifier may be configured to amplify all threesignals coming in; then the receiving digital signal processor, afteranalog-to-digital conversion, separates the signals at the other end.FIG. 20C illustrates a filter for each frequency—so the signals may beseparated back out at such stage.

Referring to FIG. 21, electromagnetic (“EM”) tracking updating isrelatively “expensive” in terms of power for a portable system, and maynot be capable of very high frequency updating. In a “sensor fusion”configuration, more frequently updated localization information or otherdynamic inputs (measurable metrics that change over time) from anothersensor such as an IMU may be combined, along with data from anothersensor, such as an optical sensor (such as a camera or depth camera),which may or may not be at a relatively high frequency; the net offusing all of these inputs places a lower demand upon the EM system andprovides for quicker updating. With further regard to “dynamic inputs,”other illustrative examples include temperature fluctuations, audiovolume, sizing such as dimensions of or distance to certain objects, andnot merely position or orientation of a user. A set of dynamic inputsrepresents a collection of those inputs as a function of a givenvariable (such as time).

Referring back to FIG. 11B, a distributed sensor coil configuration wasshown. Referring to FIG. 22A, a configuration with a singleelectromagnetic sensor device (604), such as a box containing threeorthogonal coils, one for each direction of X, Y, Z, may be coupled tothe wearable component (58) for 6 degree of freedom tracking, asdescribed above. Also as noted above, such a device may bedis-integrated, with the three sub-portions (i.e., coils) attached atdifferent locations of the wearable component (58), as shown in FIG.22B. Referring to FIG. 22C, to provide further design alternatives, eachindividual coil may be replaced with a group of similarly orientedcoils, such that the overall magnetic flux for any given orthogonaldirection is captured by the group (148, 150, 152) rather than by asingle coil for each orthogonal direction. In other words, rather thanone coil for each orthogonal direction, a group of smaller coils may beutilized and their signals aggregated to form the signal for thatorthogonal direction.

Referring to FIGS. 23A-23C, it may be useful to recalibrate a wearablecomputing system such as those discussed herein from time to time, andin one embodiment, ultrasonic signals at the transmitter, along with amicrophone at the receiver and acoustic time of flight calculation, maybe utilized to determine sound propagation delay. FIG. 23A shows that inone embodiment, 3 coils on the transmitter are energized with a burst ofsinewaves, and at the same time an ultrasonic transducer may beenergized with a burst of sinewave, preferably of the same frequency asone of the coils. FIG. 23B illustrates that a receiver may be configuredto receive the 3 EM waves using sensor coils, and the ultrasonic waveusing a microphone device. Total distance may be calculated from theamplitude of the 3 EM signals; then time of flight may be calculated bycomparing the timing of the microphone response with that of the EMcoils (FIG. 23C). This may be used to calculate distance and calibratethe EM correction factors.

Referring to FIG. 24A, in another embodiment, in an augmented realitysystem featuring a camera, the distance may be calculated by measuringthe size in pixels of a known-size feature on another device such as ahandheld controller.

Referring to FIG. 24B, in another embodiment, in an augmented realitysystem featuring a depth sensor, such as an infrared (“IR”) depthsensor, the distance may be calculated by such depth sensor and reporteddirectly to the controller.

Referring to FIGS. 24C and 24D, once total distance is known, either thecamera or the depth sensor can be used to determine position in space.The augmented reality system may be configured to project one or morevirtual targets to the user.

The user may align the controller to the targets, and the systemcalculates position from both the EM response, and from the direction ofthe virtual targets plus the previously calculated distance. Roll anglecalibration may be done by aligning a known feature on the controllerwith a virtual target projected to the user; yaw and Pitch angle may becalibrated by presenting a virtual target to the user and having theuser align two features on the controller with the target (much likesighting a rifle).

Referring to FIGS. 25A and 25B, there is an inherent ambiguityassociated with EM tracking systems of a 6 degree of freedom devices: areceiver would generate a similar response in two diagonally opposedlocations around the transmitter. Such a challenge is particularlyrelevant in systems wherein both the transmitter and receiver may bemobile relative to each other.

For six degree of freedom (DOF) tracking, a totem 524, which may also bereferred to as a handheld controller (e.g., TX), may generate EM signalsmodulated on three separate frequencies, one for each axis X, Y, and Z.A wearable 522 as illustrated in FIG. 25A, which may be implemented asan AR headset 528 as illustrated in FIG. 25B, has an EM receivingcomponent that is configured to receive EM signals on the X, Y, and Zfrequencies. The position and orientation (i.e., pose) of the totem 524can be derived based on the characteristics of the received EM signals.However, due to the symmetric nature of the EM signals, it may not bepossible to determine where the totem is located (e.g., in front of theuser or behind the user as illustrated with a ghost position 526)without an additional reference frame. That is, the same EM values canbe obtained at the wearable 522, 528 for two diametrically opposed totemposes: the position of totem 524 in a first (e.g., front) hemisphere 542and ghost position 526 in a second (e.g., rear) hemisphere, with achosen plane 540 passing through the center of a sphere dividing the twohemispheres. Accordingly, for a single snapshot of received EM signalsby wearable 522, 528, either pose is valid. However, when totem 524 ismoved, the tracking algorithm will typically encounter errors if thewrong hemisphere is chosen due to inconsistency in the data from thevarious sensors. The hemisphere ambiguity arises in part due to the factthat when a six DOF tracking session is started, the initial EM totemdata does not have an unequivocal absolute position. Instead, itprovides a relative distance, which can be interpreted as one of twopositions in the 3D volume that is divided into two equal hemisphereswith the wearable (e.g., the AR headset mounted on the head of the user)centered between the two hemispheres. Thus, embodiments of the presentinvention provide methods and systems that resolve hemisphere ambiguityin order to enable successful tracking of the actual position of thetotem.

Such ambiguity is well documented by Kuipers, where EM signals(themselves an alternating current sine output) are described as 3×3matrix S, as a function of the EM receiver in the EM transmittercoordinate frame:S=f(T,r)where T is the rotation matrix from the transmitter coordinate frame tothe receiver coordinate frame, and r is the EM receiver position in theEM transmitter coordinate frame. As Kuipers solution points out,however, solving for any 6 DOF position involves squaring a sinefunction, where a −1 position will solve for a +1 position as well,thereby creating the hemisphere ambiguity problem.

In one embodiment, one may use an IMU sensor to see if the totem 524(e.g., the EM transmitter) is on the plus or the negative side of thesymmetry axis. In an embodiment such as those described above whichfeature world cameras and a depth camera, one can use that informationto detect whether the totem 524 is in the positive side or negative sideof the reference axis. If the totem 524 is outside of the field of viewof the camera and/or depth sensor, the system may be configured todecide (or the user may decide) that the totem 524 must be in the 180°zone directly behind the user, for example.

Conventionally in the art, an EM receiver (provided in the wearable 528)is defined in the EM transmitter (provided in the totem 524) coordinateframe (as illustrated in FIG. 25C). To correct for the dual possiblereceiver locations (i.e., the actual receiver location 528 and the falsereceiver location 530, which is symmetric of the actual location of thewearable 528 with respect to the transmitter 524), a designatedinitialization position is established to localize the device (i.e., thewearable 528) to a specific reference position, and to dismiss thealternative solution (i.e., the false receiver location 530). Suchrequired start position may, however, conflict with the intended ordesired use.

Instead, in some embodiments, the EM transmitter (provided in the totem524) is defined in the EM receiver (provided in the wearable 528)coordinate frame, as illustrated in FIG. 25D. For such embodiments, theKuipers solution is modified such that the transpose of S is solved for.The consequence of this S-matrix transpose operation is equivalent totreating the EM transmitter measurement as if it were the EM receiver's,and vice versa. Notably, the same hemisphere ambiguity phenomenon stillexists, but is instead represented in the EM receiver's coordinateframe. That is, the possible transmitter locations are identified as theactual location of the totem 524 and the ghost position 526 (alsoreferred to as a false transmitter location), which is symmetric to theactual location of the totem 524 with respect to the wearable 528.

As opposed to the ambiguous hemisphere appearing both at the user'slocation and at a false location opposite the transmitter, the EMtracking system now has a hemisphere 542 in front of the user and ahemisphere 544 behind the user, as illustrated in FIG. 25E. By placingthe hemisphere boundary (e.g., the switching plane) 540 centered at thistransposed receiver location (e.g., the location of the wearable 528), aplane normal vector n_(s) 532 comes from the origin (i.e., at thereceiver placed in the wearable 528), and is directed at a predeterminedangle α 534 from a straight (horizontal) line taken from the wearable528. In some embodiments, the predetermined angle α 534 may be at 45°from the straight line taken from the wearable 528. In otherembodiments, the predetermined angle α 534 may be at other anglesmeasured with respect to a straight line taken from the wearable 528 andthe use of 45° is merely exemplary. Generally, if a user is holdingtotem 524 at waist level, the totem will generally be in the vicinity ofthe plane normal vector n_(s) 532 oriented at an angle of approximately45° from the straight line taken from the wearable 528. One of ordinaryskill in the art would recognize many variations, modifications, andalternatives.

If the totem 524 is within the hemisphere in front of the user (i.e.,the hemisphere 542 in FIG. 25E), then the plane normal vector n_(s) 532the position vector p representing a position of the totem 524 willsatisfy a dot product inequality relationship of n_(s)·p>0, where p isthe position point of a vector defined in the receiver coordinate frame.The vector p 536 may be a vector connecting the transmitter provided atthe totem 524 to the receiver provided at the wearable 528. For example,the vector p 536 may be provided at a predetermined angle β 538 from thenormal vector n_(s) 532. When the position of the transmitter (at thetotem 524) is determined in the receiver coordinate frame, two positionpoint vectors are identified: the first (positive) position point vectorp 536, and a second (negative) position point vector 546 which issymmetric to the first position point vector p 536 with respect to thereceiver (at the wearable 528).

To determine which hemisphere a device (e.g., the totem 524) falls in,the two possible solutions of the S matrix are applied to the dotproduct, and the positive number yields the correct hemisphere location.Such determination is anecdotally true as well for the described systemand use cases within, as a user is very unlikely to be holding a deviceat arms-length behind them.

FIG. 25F illustrates a flowchart 550 describing determining a correctpose of a hand-held controller using a position vector for the hand-helddevice relative to the headset according to some embodiments. Accordingto various embodiments, the steps of the flowchart 550 may be performedduring an initialization process of the headset. At step 552, thehand-held controller of an optical device system emits one or moremagnetic fields. At step 554, the one or more sensors positioned withina headset (e.g., a wearable by a user) of the system detects the one ormore magnetic fields emitted by the hand-held controller. At step 556, aprocessor coupled to the headset determines a first position and a firstorientation of the hand-held controller within a first hemisphere withrespect to the headset based on the one or more magnetic fields. At step558, the processor determines a second position and a second orientationof the hand-held controller within a second hemisphere with respect tothe headset based on the one or more magnetic fields. The secondhemisphere is diametrically opposite the first hemisphere with respectto the headset. At step 560, the processor determines (defines) a normalvector with respect to the headset, and a position vector identifying aposition of the hand-held controller with respect to the headset in thefirst hemisphere. The position vector is defined at a coordinate frameof the headset. The normal vector originates from the headset andextends at a predetermined angle from a horizontal line from theheadset. In some embodiments, the predetermined angle is 45° angle downfrom the horizontal line from the headset. At step 562, the processorcalculates a dot-product of the normal vector and the position vector.In some examples, upon calculating the dot-product of the normal vectorand the position vector, the processor may determine whether a result ofthe dot-product is positive or negative in value.

When a result of the dot-product is determined to be positive in value,the processor determines that the first position and the firstorientation of the hand-held controller is accurate at step 564.Accordingly, when result of the dot-product is positive, the firsthemisphere is identified as a front hemisphere with respect to theheadset and the second hemisphere is identified as a back hemispherewith respect to the headset. The system may then deliver virtual contentto a display of the system based on the first position and the firstorientation when the result of the dot-product is positive.

When the result of the dot-product is determined to be negative invalue, the processor determines that the second position and the secondorientation of the hand-held controller is accurate at step 566.Accordingly, wherein when the result of the dot-product is negative, thesecond hemisphere is identified as a front hemisphere with respect tothe headset and the first hemisphere is identified as a back hemispherewith respect to the headset. The system may then deliver virtual contentto a display of the system based on the second position and the secondorientation when the result of the dot-product is negative. In someembodiments, Step 566 is not performed if the dot-product is determinedto be positive (i.e., step 566 may be an optional step). Similarly, insome embodiments, step 558 is not performed if the dot-product isdetermined to be positive (i.e., step 558 may be an optional step).

Referring back to the embodiments above wherein outward-oriented cameradevices (124, 154, 156) are coupled to a system component such as a headmounted component (58), the position and orientation of the head coupledto such head mounted component (58) may be determined using informationgathered from these camera devices, using techniques such assimultaneous localization and mapping, or “SLAM” techniques (also knownas parallel tracking and mapping, or “PTAM” techniques).

Understanding the position and orientation of the head of the user, alsoknown as the user's “head pose”, in real or near-real time (i.e.,preferably with low latency of determination and updating) is valuablein determining where the user is within the actual environment aroundhim or her, and how to place and present virtual content relative to theuser and the environment pertinent to the augmented or mixed realityexperience of the user. A typical SLAM or PTAM configuration involvesextracting features from incoming image information and using this totriangulate 3-D mapping points, and then tracking against those 3-Dmapping points. SLAM techniques have been utilized in manyimplementations, such as in self-driving cars, where computing, power,and sensing resources may be relatively plentiful when compared withthose which might be available on board a wearable computing device,such as a head mounted component (58).

Referring to FIG. 26, in one embodiment, a wearable computing device,such as a head mounted component (58), may comprise two outward-facingcameras producing two camera images (left—204, right—206). In oneembodiment a relatively lightweight, portable, and power efficientembedded processor, such as those sold by Movidius®, Intel®, Qualcomm®,or Ceva®, may comprise part of the head mounted component (58) and beoperatively coupled to the camera devices. The embedded processor may beconfigured to first extract features (210, 212) from the camera images(204, 206). If the calibration between the two cameras is known, thenthe system can triangulate (214) 3-D mapping points of those features,resulting in a set of sparse 3-D map points (202). This may be stored asthe “map”, and these first frames may be utilized to establish the“world” coordinate system origin (208). As subsequent image informationcomes into the embedded processor from the cameras, the system may beconfigured to project the 3-D map points into the new image information,and compare with locations of 2-D features that have been detected inthe image information. Thus the system may be configured to attempt toestablish a 2-D to 3-D correspondence, and using a group of suchcorrespondences, such as about six of them, the pose of the user's head(which is, of course, coupled to the head mounted device 58) may beestimated. A greater number of correspondences, such as more than six,generally means a better job of estimating the pose. Of course thisanalysis relies upon having some sense of where the user's head was(i.e., in terms of position and orientation) before the current imagesbeing examined. As long as the system is able to track without too muchlatency, the system may use the pose estimate from the most immediatelyprevious time to estimate where the head is for the most current data.Thus is the last frame was the origin, the system may be configured toestimate that the user's head is not far from that in terms of positionand/or orientation, and may search around that to find correspondencesfor the current time interval. Such is a basis of one embodiment of atracking configuration.

After moving sufficiently away from the original set of map points(202), one or both camera images (204, 206) may start to lose the mappoints in the newly incoming images (for example, if the user's head isrotating right in space, the original map points may start to disappearto the left and may only appear in the left image, and then not at allwith more rotation). Once the user has rotated too far away from theoriginal set of map points, the system may be configured to create newmap points, such as by using a process similar to that described above(detect features, create new map points)—this is how the system may beconfigured to keep populating the map. In one embodiment, this processmay be repeated again every 10 to 20 frames, depending upon how much theuser is translating and/or rotating his head relative to hisenvironment, and thereby translating and/or rotating the associatedcameras. Frames associated with newly created mapping points may bedeemed “key frames,” and the system may be configured to delay thefeature detection process with key frames, or alternatively, featuredetection may be conducted upon each frame to try to establish matches,and then when the system is ready to create a new key frame, the systemalready has that associated feature detection completed. Thus, in oneembodiment, the basic paradigm is to start off creating a map, and thentrack, track, track until the system needs to create another map oradditional portion thereof.

Referring to FIG. 27A, in one embodiment, vision based pose calculationmay be split into 5 stages (pre-tracking 216, tracking 218, low-latencymapping 220, latency-tolerant mapping 222, post mapping/cleanup 224) toassist with precision and optimization for embedded processorconfigurations wherein computation, power, and sensing resources may belimited.

With regard to pretracking (216), the system may be configured toidentify which map points project into the image before the imageinformation arrives. In other words, the system may be configured toidentify which map points would project into the image given that thesystem knows where the user was before, and has a sense or where theuser is going.

The notion of “sensor fusion” is discussed further below, but it isworth noting here that one of the inputs that the system may get from asensor fusion module or functionality may be “post estimation”information, at a relatively fast rate, such as at 250 Hz from aninertial measurement unit (“IMU”) or other sensor or device (this is ahigh rate relative to, say, 30 Hz, at which the vision based posecalculation operation may be providing updates). Thus there may be amuch finer temporal resolution of pose information being derived fromIMU or other device relative to vision based pose calculation; but it isalso noteworthy that the data from devices such as IMUS tends to besomewhat noisy and susceptible to pose estimation drift, as discussedbelow. For relatively short time windows, such as 10-15 milliseconds,the IMU data may be quite useful in predicting pose, and, again, whencombined with other data in a sensor fusion configuration, an optimizedoverall result may be determined. For example, and as explained ingreater detail below with reference to FIG. 28B-28G, a propagation pathof collected points may be adjusted, and from that adjustment a pose ata later time may be estimated/informed by where sensor fusion determinesa future point may be based on the adjusted propagation path.

Pose information coming from a sensor fusion module or functionality maybe termed “pose prior”, and this pose prior may be utilized by thesystem to estimate which sets of points are going to project into thecurrent image. Thus in one embodiment, the system is configured in a“pre tracking” step (216) to pre-fetch those map points and conduct somepre-processing that helps to reduce latency of overall processing. Eachof the 3-D map points may be associated with a descriptor, so that thesystem may identify them uniquely and match them to regions in theimage. For example, if a given map point was created by using a featurethat has a patch around it, the system may be configured to maintainsome semblance of that patch along with the map point, so that when themap point is seen projected onto other images, the system can look backat the original image used to create the map, examine the patchcorrelation, and determine if they are the same point. Thus inpre-processing, the system may be configured to do some amount offetching of map points, and some amount of pre-processing associatedwith the patches associated with those map points. Thus in pre-tracking(216), the system may be configured to pre-fetch map points, andpre-warp image patches (a “warp” of an image may be done to ensure thatthe system can match the patch associated with the map point with thecurrent image; it's a way to make sure that the data being compared iscompatible).

Referring back to FIG. 27, a tracking stage may comprise severalcomponents, such as feature detection, optical flow analysis, featurematching, and pose estimation. While detecting features in the incomingimage data, the system may be configured to utilize optical flowanalysis to save computational time in feature detection by trying tofollow features from one or more previous images. Once features havebeen identified in the current image, the system may be configured totry to match the features with projected map points—this may be deemedthe “feature matching” portion of the configuration. In the pre-trackingstage (216), the system preferably has already identified which mappoints are of interest, and fetched them; in feature mapping, they areprojected into the current image and the system tries to match them withthe features. The output of feature mapping is the set of 2-D to 3-Dcorrespondences, and with that in hand, the system is configured toestimate the pose.

As the user is tracking his head around, coupled to the head mountedcomponent (58), the system preferably is configured to identify if theuser is looking at a new region of the environment or not, to determinewhether a new key frame is needed. In one embodiment, such analysis ofwhether a new key frame is needed may be almost purely based upongeometry; for example, the system may be configured to look at thedistance (translational distance; also field-of-view capturereorientation—the user's head may be close translationally butre-oriented such that completely new map points are required, forexample) from the current frame to the remaining key frames. Once thesystem has determined that a new key frame should be inserted, themapping stage may be started. As noted above, the system may beconfigured to operate mapping as three different operations (low-latencymapping, latency-tolerant mapping, post/mapping or cleanup), as opposedto a single mapping operation more likely seen in a conventional SLAM orPTAM operation.

Low-latency mapping (220), which may be thought of in a simplistic formas triangulation and creation of new map points, is a critical stage,with the system preferably configured to conduct such stage immediately,because the paradigm of tracking discussed herein relies upon mappoints, with the system only finding a position if there are map pointsavailable to track against. The “low-latency” denomination refers to thenotion that there is no tolerance for unexcused latency (in other words,this part of the mapping needs to be conducted as quickly as possible orthe system has a tracking problem).

Latency-tolerant mapping (222) may be thought of in a simplistic form asan optimization stage. The overall process does not absolutely requirelow latency to conduct this operation known as “bundle adjustment”,which provides a global optimization in the result. The system may beconfigured to examine the positions of 3-D points, as well as where theywere observed from. There are many errors that can chain together in theprocess of creating map points. The bundle adjustment process may take,for example, particular points that were observed from two differentview locations and use all of this information to gain a better sense ofthe actual 3-D geometry.

The result may be that the 3-D points and also the calculated trajectory(i.e., location, path of the capturing cameras) may be adjusted by asmall amount. It is desirable to conduct these kinds of processes to notaccumulate errors through the mapping/tracking process.

The post mapping/cleanup (224) stage is one in which the system may beconfigured to remove points on the map that do not provide valuableinformation in the mapping and tracking analysis. In this stage, thesepoints that do not provide useful information about the scene areremoved, and such analysis is helpful in keeping the entire mapping andtracking process scaleable.

During the vision pose calculation process, there is an assumption thatfeatures being viewed by the outward-facing cameras are static features(i.e., not moving from frame to frame relative to the global coordinatesystem). In various embodiments, semantic segmentation and/or objectdetection techniques may be utilized to remove moving objects from thepertinent field, such as humans, moving vehicles, and the like, so thatfeatures for mapping and tracking are not extracted from these regionsof the various images. In one embodiment, deep learning techniques, suchas those described below, may be utilized for segmenting out thesenon-static objects.

In some embodiments, a pre-fetching protocol lead to pose estimationaccording to method (2710) as depicted in FIG. 27B. From 2710 to 2720pose data is received over time, such that an estimated pose at a futuretime may be extrapolated at 2730. Extrapolation may be simple constantvalue extrapolation from given inputs, or corrected extrapolation basedon correction input points as described below with reference to sensorfusion.

Upon determining an estimated future pose, in some embodiments thesystem accesses a feature map for that position. For example, if a useris walking while wearing a head mounted component, the system mayextrapolate a future position based on the pace of the user and access afeature map for that extrapolated/estimated position. In someembodiments, this step is pre-fetching as described above with referenceto step (216) of FIG. 27A. At (2740) specific points of the feature mapare extracted, and in some embodiments, patches surrounding the pointsare extracted as well. At (2750) the extracted points are processed. Insome embodiments, processing comprises warping the points to match anestimated orientation or find a homography of the extracted points orpatches.

At (2760) a real time image (or the current view at the estimated time)is received by the system. The processed points are projected onto thereceived image at (2770). At 2780 the system establishes correspondencesbetween the received image and the processed points. In some cases, thesystem has made a perfect estimation and the received image andprocessed points align perfectly, confirming the estimated pose of(2730). In other cases, the processed points do not perfectly align withfeatures of the received images and the system performs additionalwarping or adjustments to determine the correct pose based on a degreeof correspondence at (2790). Of course, it is possible that none of theprocessed points align with features in the received image, and thesystem would have to revert to new tracking and feature mapping asdescribed above with reference to FIG. 27A and steps (218)-(224).

Referring to FIGS. 28A-28F, a sensor fusion configuration may beutilized to benefit from one source of information coming from a sensorwith relatively high update frequency (such as an IMU updating gyro,accelerometer, and/or magnetometer data pertinent to head pose at afrequency such as 250 Hz) and another information source updating at alower frequency (such as a vision based head pose measurement processupdating at a frequency such as 30 Hz).

Referring to FIG. 28A, in one embodiment the system may be configured touse an extended Kalman filter (“EKF”, 232) and to track a significantamount of information regarding the device. For example, in oneembodiment, it may account for 32 states, such as angular velocity(i.e., from the IMU gyroscope), translational acceleration (i.e., fromthe IMU accelerometers), calibration information for the IMU itself(i.e., coordinate systems and calibration factors for the gyros andaccelerometers; the IMU may also comprise one or more magnetometers).Thus the system may be configured to take in IMU measurements at arelatively high update frequency (226), such as 250 Hz, as well as datafrom some other source at a lower update frequency (i.e., calculatedvision pose measurement, odometry data, etc.), here vision posemeasurement (228) at an update frequency such as 30 Hz.

Each time the EKF gets a round of IMU measurements, the system may beconfigured to integrate the angular velocity information to getrotational information (i.e., the integral of angular velocity (changein rotational position over change in time) is angular position (changein angular position)); likewise for translational information (in otherwords, by doing a double integral of the translational acceleration, thesystem will get position data). With such calculation the system isconfigured to get six degree-of-freedom (“DOF”) pose information fromthe head (translation in X, Y, Z; orientation for the three rotationalaxes)—at the high frequency from the IMU (i.e., 250 Hz in oneembodiment). Each time an integration is done, noise is accumulated inthe data; doing a double integration on the translational or rotationalacceleration can propagate noise.

Generally the system is configured to not rely on such data which issusceptible to “drift” due to noise for too long a time window, such asany longer than about 100 milliseconds in one embodiment. The incominglower frequency (i.e., updated at about 30 Hz in one embodiment) datafrom the vision pose measurement (228) may be utilized to operate as acorrection factor with the EKF (232), producing a corrected output(230).

FIGS. 28B-28F illustrate how the data from one source at a higher updatefrequency may be combined with the data from another source at a lowerupdate frequency. As depicted in FIG. 28B, a first group of dynamicinput points (234), for example from a from an IMU at a higherfrequency, such as 250 Hz, is shown, with a correction input point (238)coming in at a lower frequency, such as 30 Hz, such as from a visionpose calculation process. The system may be configured to correct (242)to the vision pose calculation point when such information is available,and then continue forward with a second set of dynamic input points(236) such as points from the IMU data and another correction (244) fromanother correction input point (240) available from the vision posecalculation process. Stated differently, the high frequency dynamicinputs collected from a first sensor may be periodically adjusted by alow frequency corrective input collected from a second sensor.

In this way, embodiments of the present invention applying corrective“updates” with the vision pose data to the “propagation path” of datacoming from the IMU, using an EKF. Such update, as depicted in FIG. 28B,adjusts the propagation path of dynamic inputs (234) to a new origin atthe correction input point (238), again at (240), and so on for futurecollection of points. In some embodiments, rather than adjust apropagation path to originate from a correction input point, apropagation path may adjusted by changing a rate of change (i.e., slopeof points (236) in FIG. 28B) for the second set of dynamic inputs by acalculated coefficient. These adjustments may reduce jitter in thesystem by interpreting new data collection as less severe differencesfrom a corrective input.

For example, as depicted in FIG. 28B-2, a coefficient is applied tosecond set of dynamic input points (236) such that the origin of thepropagation path is the same as the last point of the propagation pathof the first set of dynamic input points (234), but sloped less severeas compared to the rate of change of first set of dynamic input points(234). As depicted in FIG. 28B-2, the size of corrections (242) and(242) changes, but not as severe as if there had been no correctioninput at time T_(N), and depending on the computing resource and sensor,making two full corrections at T_(N) and T_(N+M) as shown in FIG. 28B-1may be more computationally expensive and prone to introduce jitterbetween the sensors than the more subtle adjustment of FIG. 28B-2.Further, as depicted in FIG. 28B, correction input points (238) and(240) are in the same location; if those points were also moving, a usermay benefit from having a slope adjustment of dynamic input points thatplaces the last point of propagation path (236) closer to correctioninput point (240), as compared to an origination adjustment.

FIG. 28G depicts method 2800 of collecting a first set of dynamic pointsat (2810), and then collecting a correction input point at (2820). Insome embodiments, method 2800 proceeds to step (2830 a) where a secondset of dynamic points is collected, and the resulting propagation pathof those collected points are adjusted at (2830 b) based on thecorrection input (such as by adjusting a recorded rate of change, oradjusting an origin of that propagation path). In some embodiments,after the correction input point is collected at (2820) an adjustment isdetermined at (2830 b) to be applied to dynamic points collectedthereafter and at (2830 a), as second set of dynamic input points arecollected the propagation path is adjusted in real time.

For systems employing sensors known to produce compounding error/noiseover time and other sensors that produce low error/noise, such sensorfusion provides more economical computing resource management.

In is notable that in some embodiments, the data from the second source(i.e., such as the vision pose data) may come in not only at a lowerupdate frequency, but also with some latency—meaning that the systempreferably is configured to navigate a time domain adjustment as theinformation from IMU and vision pose calculation are integrated. In oneembodiment, to ensure that the system is fusing in the vision posecalculation input at the correct time domain position in the IMU data, abuffer of IMU data may be maintained, to go back, to a time (say “Tx”)in the IMU data to do the fusion and calculate the “update” oradjustment at the time pertinent to the input from the vision posecalculation, and then account for that in forward propagation to thecurrent time (say “Tcurrent”), which leaves a gap between the adjustedposition and/or orientation data and the most current data coming fromthe IMU. To ensure that there is not too much of a “jump” or “jitter” inthe presentation to the user, the system may be configured to usesmoothing techniques. One way to address this issue is to use weightedaveraging techniques, which may be linear, nonlinear, exponential, etc.,to eventually drive the fused data stream down to the adjusted path.Referring to FIG. 28C, for example, weighted averaging techniques may beutilized over the time domain between T0 and T1 to drive the signal fromthe unadjusted path (252; i.e., coming straight from the IMU) to theadjusted path (254; i.e., based upon data coming from the visual posecalculation process); one example is shown in FIG. 28D, wherein a fusedresult (260) is shown starting at the unadjusted path (252) and time TOand moving exponentially to the adjusted path (254) by T1. Referring toFIG. 28E, a series of correction opportunities is shown with anexponential time domain correction of the fused result (260) toward thelower path from the upper path in each sequence (first correction isfrom the first path 252, say from the IMU, to the second path 254, sayfrom vision based pose calculation; then continuing with the similarpattern forward, using the continued IMU data while correcting, down inthis example toward successive corrected lower paths 256, 258 based uponsuccessive points from vision pose, using each incoming vision basedpose calculation point). Referring to FIG. 28F, with short enough timewindows between the “updates” or corrections, the overall fused result(260) functionally may be perceived as a relatively smooth patternedresult (262).

In other embodiment, rather than rely directly upon the vision posemeasurement, the system may be configured to examine the derivative EKF;in other words, rather than using vision pose calculation resultdirectly, the system uses the change in vision pose from the currenttime to the previous time. Such a configuration may be pursued, forexample, if the amount of noise in the vision pose difference is a lotless than the amount of noise in the absolute vision pose measurement.It is preferable to not have instantaneous errors throwing off the fusedresult, because the output of all of this is pose, which gets sent backas the “pose prior” values to the vision system.

The external system-based “consumer” of the pose result may be termedthe “Pose Service”, and the system may be configured such that all othersystem components tap into the Pose Service when requesting a pose atany given time. The Pose Service may be configured to be a queue orstack (i.e., a buffer), with data for a sequences of time slices, oneend having the most recent data. If a request of the Pose Service is thecurrent pose, or some other pose that is in the buffer, then it may beoutputted immediately; in certain configurations, the Pose Service willreceive a request for: what is the pose going to be 20 millisecondsforward in time from now (for example, in a video game content renderingscenario—it may be desirable for a related service to know that it needsto be rendering something in a given position and/or orientationslightly in the future from now). In one model for producing a futurepose value, the system may be configured to use a constant velocityprediction model (i.e., assume that the user's head is moving with aconstant velocity and/or angular velocity); in another model forproducing a future pose value, the system may be configured to use aconstant acceleration prediction model (i.e., assume that the user'shead is translating and/or rotating with constant acceleration).

The data in the data buffer may be utilized to extrapolate where thepose will be using such models. A constant acceleration model uses a bitlonger tail into the data of the buffer for prediction than does aconstant velocity model, and we have found that the subject systems canpredict into the range of 20 milliseconds in the future withoutsubstantial degradation. Thus the Pose Service may be configured to havea data buffer going back in time, as well as about 20 milliseconds ormore going forward, in terms of data that may be utilized to outputpose.

Operationally, content operations generally will be configured toidentify when the next frame draw is going to be coming in time (forexample, it will either try to draw at a time T, or at a time T+N, the Nbeing the next interval of updated data available from the PoseService).

The use of user-facing (i.e., inward-facing, such as toward the user'seyes) cameras, such as those depicted in FIG. 16B (14) may be utilizedto conduct eye tracking, as described, for example, in U.S. patentapplication Ser. Nos. 14/707,000 and 15/238,516, which are incorporatedby reference herein in their entirety. The system may be configured toconduct several steps in eye tracking, such as first taking an image ofthe eye of the user; then using segmenting analysis to segment anatomyof the eye (for example, to segment the pupil, from the iris, from thesclera, from the surrounding skin); then the system may be configured toestimate the pupil center using glint locations identified in the imagesof the eye, the glints resulting from small illumination sources (16),such as LEDs, which may be placed around the inward-facing side of thehead mounted component (58); from these steps, the system may beconfigured to use geometric relationships to determine an accurateestimate regarding where in space the particular eye is gazing. Suchprocesses are fairly computationally intensive for two eyes,particularly in view of the resources available on a portable system,such as a head mounted component (58) featuring on on-board embeddedprocessor and limited power. Deep learning techniques may be trained andutilized to address these and other computational challenges.

For example, in one embodiment, a deep learning network may be utilizedto conduct the segmentation portion of the aforementioned eye trackingparadigm (i.e., a deep convolutional network may be utilized for robustpixel-wise segmentation of the left and right eye images into iris,pupil, sclera, and rest classes), with everything else remaining thesame; such a configuration takes one of the large computationallyintensive portions of the process and makes it significantly moreefficient. In another embodiment, one joint deep learning model may betrained and utilized to conduct segmentation, pupil detection, and glintdetection (i.e., a deep convolutional network may be utilized for robustpixel-wise segmentation of the left and right eye images into iris,pupil, sclera, and rest classes; eye segmentation may then be utilizedto narrow down the 2-D glint locations of active inward-facing LEDillumination sources); then the geometry calculations to determine gazemay be conducted. Such a paradigm also streamlines computation. In athird embodiment, a deep learning model may be trained and utilized todirectly estimate gaze based upon the two images of the eyes coming fromthe inward-facing cameras (i.e., in such an embodiment, a deep learningmodel solely using the pictures of the user's eyes may be configured totell the system where the user is gazing in three dimensional space; adeep convolutional network may be utilized for robust pixel-wisesegmentation of the left and right eye images into iris, pupil, sclera,and rest classes; eye segmentation may then be utilized to narrow downthe 2-D glint locations of active inward-facing LED illuminationsources; the 2-D glint locations along with 3-D LED locations may beutilized to detect the cornea center in 3-D; note that all 3-D locationsmay be in the respective camera coordinate system; then eye segmentationmay also be utilized to detect the pupil center in the 2-D image usingellipse fitting; using offline calibration information, the 2-D pupilcenter may be mapped to a 3-D gaze point, with depth being determinedduring calibration; the line connecting the cornea 3-D location and the3-D gaze point location is the gaze vector for that eye); such aparadigm also streamlines computation, and the pertinent deep networkmay be trained to directly predict the 3-D gaze point given the left andright images. The loss function for such deep network to perform such atraining may be a simple Euclidean loss, or also include the well-knowngeometric constraints of the eye model.

Further, deep learning models may be included for biometricidentification using images of the user's iris from the inward-facingcameras. Such models may also be utilized to determine if a user iswearing a contact lens—because the model will jump out in the Fouriertransform of the image data from the inward-facing cameras.

The use of outward-facing cameras, such as those depicted in FIG. 16A(124, 154, 156) may be utilized to conduct SLAM or PTAM analysis for thedetermination of pose, such as the pose of a user's head relative to theenvironment in which he is present wearing a head-mounted component(58), as described above. Most SLAM techniques are dependent upontracking and matching of geometric features, as described in theembodiments above. Generally it is helpful to be in a “textured” worldwherein the outward-facing cameras are able to detect corners, edges,and other features; further, certain assumptions may be made about thepermanence/statics of features that are detected in scenes, and it ishelpful to have significant computing and power resources available forall of this mapping and tracking analysis with SLAM or PTAM processes;such resources may be in short supply with certain systems, such as someof those which are portable or wearable, and which may have limitedembedded processing capabilities and power available. Deep learningnetworks may be incorporated into various embodiments to observedifferences in image data, and based upon training and configuration,play a key role in the SLAM analysis (in the context of SLAM, the deepnetworks herein may be deemed “DeepSLAM” networks) of variations of thesubject system.

In one embodiment, a DeepSLAM network may be utilized to estimate posebetween a pair of frames captured from cameras coupled to a component tobe tracked, such as the head mounted component (58) of an augmentedreality system. The system may comprise a convolutional neural networkconfigured to learn transformation of pose (for example, the pose of ahead mounted component 58) and apply this in a tracking manner. Thesystem may be configured to start looking at a particular vector andorientation, such as straight ahead at a known origin (so 0,0,0 as X, Y,Z). Then the user's head may be moved, for example, to the right a bit,then to the left a bit between frame 0 and frame 1 with the goal ofseeking the pose transform or relative pose transformation. Theassociated deep network may be trained on a pair of images, for example,wherein we know pose A and pose B, and image A and image B; this leadsto a certain pose transformation. With the pose transformationdetermined, one may then integrate associated IMU data (fromaccelerometers, gyros, etc.—as discussed above) into the posetransformation and continue tracking as the user moves away from theorigin, around the room, and at whatever trajectory. Such a system maybe termed a “relative pose net”, which as noted above, is trained basedupon pairs of frames wherein the known pose information is available(the transformation is determined from one frame to the other, and basedupon the variation in the actual images, the system learns what the posetransformation is in terms of translation and rotation). Deep homographyestimation, or relative pose estimation, has been discussed, forexample, in U.S. Patent Application Ser. No. 62/339,799, which isincorporated by reference herein in its entirety.

When such configurations are utilized to conduct pose estimation fromframe 0 to frame 1, the result generally is not perfect, and the systemmust have a means for dealing with drift. As the system moves forwardfrom frame 1 to 2 to 3 to 4 and estimates relative pose, there is asmall amount of error brought in between each pair of frames. This errorgenerally accumulates and becomes a problem (for example, withoutaddressing this error-based drift, the system can end up placing theuser and his or her associated system componentry in the wrong locationand orientation with pose estimation). In one embodiment, the notion of“loop closure” may be applied to solve what may be termed the“relocalization” problem. In other words, the system may be configuredto determine if it has been in a particular place before—and if so, thenthe predicted pose information should make sense in view of the previouspose information for the same location. For example, the system may beconfigured such that anytime it sees a frame on the map that has beenseen before, it relocalizes; if the translation is off, say by 5 mm inthe X direction, and the rotation is off, say by 5 degrees in the thetadirection, then the system fixes this discrepancy along with those ofthe other associated frames; thus the trajectory becomes the true one,as opposed to the wrong one. Relocalization is discussed in U.S. PatentApplication Ser. No. 62/263,529, which is incorporated by referenceherein in its entirety.

It also turns out that when pose is estimated, in particular by usingIMU information (i.e., such as data from associated accelerometers,gyros, and the like, as described above), there is noise in thedetermined position and orientation data. If such data is directlyutilized by the system without further processing to present images, forexample, there is likely to be undesirable jitter and instabilityexperienced by the user; this is why in certain techniques, such as someof those described above, Kalman filters, sensor fusion techniques, andsmoothing functions may be utilized.

With deep network solutions, such as those described above usingconvolutional neural nets to estimate pose, the smoothing issue may beaddressed using recurrent neural networks, or RNNs, which are akin to along short term memory network. In other words, the system may beconfigured to build up the convolutional neural net, and on top of that,the RNN is placed. Traditional neural nets are feed forward in design,static in time; given an image or pair of images, they give you ananswer. With the RNN, the output of a layer is added to the next inputand fed back into the same layer again—which typically is the only layerin the net; can be envisioned as a “passage through time”—at each pointin time, the same net layer is reconsidering a slightly temporally tunedinput, and this cycle is repeated.

Further, unlike feed forward nets, an RNN can receive a sequence ofvalues as an input (i.e., sequenced over time)—and can also produce asequence of values as output. The simple structure of the RNN with builtin feedback loop that allows it to behave like a forecasting engine, andthe result when combined with the convolutional neural net in thisembodiment is that the system can take relatively noisy trajectory datafrom the convolutional neural net, push it through the RNN, and it willoutput a trajectory that is much smoother, much more like human motion,such as motion of a user's head which may be coupled to a head mountedcomponent (58) of a wearable computing system.

The system may also be configured to determine depth of an object from astereo pair of images, wherein you have a deep network and left andright images are input. The convolutional neural net may be configuredto output the disparity between left and right cameras (such as betweenleft eye camera and right eye camera on a head mounted component 58);the determined disparity is the inverse of the depth if the focaldistance of the cameras is known, so the system can be configured toefficiently calculate depth having the disparity information; thenmeshing and other processes may be conducted without involvingalternative components for sensing depth, such as depth sensors, whichmay require relatively high computing and power resource loads.

As regards semantic analysis and the application of deep networks tovarious embodiments of the subject augmented reality configurations,several areas are of particular interest and applicability, includingbut not limited to detection of gestures and keypoints, facerecognition, and 3-D object recognition.

With regard to gesture recognition, in various embodiments the system isconfigured to recognize certain gestures by a user's hands to controlthe system. In one embodiment, the embedded processor may be configuredto utilize what are known as “random forests” along with sensed depthinformation to recognize certain gestures by the user. A random forestmodel is a nondeterministic model which may require a fairly largelibrary of parameters, and may require a relatively large processing andtherefore power demand.

Further, depth sensors may not always be optimally suited for readinghand gestures with certain backgrounds, such as desk or tabletops orwalls which are near to the depth of the subject hand, due to noiselimitations with certain depth sensors and inabilities to determinedifferences between, for example, 1 or 2 cm in depth accurately. Incertain embodiments, random forest type of gesture recognition may bereplaced with deep learning networks. One of the challenges in utilizingdeep networks for such a configuration is in labelling portions of theimage information, such as pixels, as “hand” or “not hand”; training andutilizing deep networks with such segmentation challenges may requiredoing segmentations with millions of images, which is very expensive andtime consuming. To address this, in one embodiment, during trainingtime, a thermal camera, such as those available for military or securitypurposes, may be coupled to the conventional outward-facing camera, suchthat the thermal camera essentially does the segmentation of “hand” and“no hand” itself by showing which portions of the image are hot enoughto be human hand, and which are not.

With regard to face recognition, and given that the subject augmentedreality system is configured to be worn in a social setting with otherpersons, understanding who is around the user may be of relatively highvalue—not only for simply identifying other nearby persons, but also foradjusting the information presented (for example, if the systemidentifies a nearby person as an adult friend, it may suggest that youplay chess and assist in that; if the system identifies a nearby personas your child, it may suggest that you go and play soccer and may assistin that; if the system fails to identify a nearby person, or identifiesthem as a known danger, the user may be inclined to avoid proximity withsuch person).

In certain embodiments, deep neural network configurations may beutilized to assist with face recognition, in a manner similar to thatdiscussed above in relation to deep relocalization. The model may betrained with a plurality of different faces pertinent to the user'slife, and then when a face comes near the system, such as near the headmounted component (58), the system can take that face image in pixelspace, translate it, for example, into a 128-dimensional vector, andthen use vectors as points in high dimensional space to FIG. out whetherthis person is present in your known list of people or not. In essence,the system may be configured to do a “nearest neighbor” search in thatspace, and as it turns out, such a configuration can be very accurate,with false positive rates running in the 1 out of 1,000 range.

With regard to 3-D object detection, in certain embodiments, it isuseful to have a deep neural network incorporated which will tell theuser about the space they are in from a 3-dimensional perspective (i.e.,not only walls, floors, ceiling, but also objects populating the room,such as couches, chairs, cabinets, and the like—not just from atraditional 2-dimensional sense, but from a true 3-dimensional sense).For example, in one embodiment it is desirable for a user to have amodel which understands the true volumetric bounds of a couch in theroom—so that the user knows what volume is occupied by the volume of thecouch in the event that a virtual ball or other object is to be thrown,for example. A deep neural network model may be utilized to form acuboid model with a high level of sophistication.

In certain embodiments, deep reinforcement networks, or deepreinforcement learning, may be utilized to learn effectively what anagent should be doing in a specific context, without the user everhaving to directly tell the agent. For example, if a user wants toalways have a virtual representation of his dog walking around the roomthat he is occupying, but he wants the dog representation to always bevisible (i.e., not hidden behind a wall or cabinet), a deepreinforcement approach may turn the scenario into a game of sorts,wherein the virtual agent (here a virtual dog) is allowed to roam aroundin the physical space near the user, but during training time, a rewardis given if the dog stays in acceptable locations from, say T0 to T1,and a penalty is given if the user's view of the dog becomes occluded,lost, or bumps into a wall or object. With such an embodiment, the deepnetwork starts learning what it needs to do to win points rather thanlose points, and pretty soon it knows what it needs to know to providethe desired function.

The system may also be configured to address lighting of the virtualworld in a manner that approximates or matches the lighting of theactual world around the user. For example, to make a virtual perceptionblend in as optimally as possible with actual perception in augmentedreality, lighting color, shadowing, and lighting vectoring is reproducedas realistically as possible with the virtual objects. In other words,if a virtual opaque coffee cup is to be positioned upon an actualtabletop in a room with yellowish tinted light coming from oneparticular corner of the room that creates shadowing from the real worldobjects on the real world table, then optimally the light tinting andshadowing of the virtual coffee cup would match the actual scenario. Incertain embodiments, a deep learning model may be utilized to learn theillumination of an actual environment in which the system component isplaced. For example, a model may be utilized that, given an image orsequences of images from the actual environment, learns the illuminationof the room to determine factors such as brightness, hue, and vectoringby one or more light sources. Such a model may be trained from syntheticdata, and from images captured from the user's device, such as from theuser's head mounted component (58).

Referring to FIG. 29, a deep learning network architecture which may becalled a “Hydra” architecture (272) is illustrated. With such aconfiguration, a variety of inputs (270), such as IMU data (fromaccelerometers, gyros, magnetometers), outward-facing camera data, depthsensing camera data, and/or sound or voice data may be channeled to amultilayer centralized processing resource having a group of lowerlayers (268) which conduct a significant portion of the overallprocessing, pass their results to a group of middle layers (266), andultimately to one or more of a plurality of associated “heads” (264)representing various process functionalities, such as face recognition,visual search, gesture identification, semantic segmentation, objectdetection, lighting detection/determination, SLAM, relocalization,and/or depth estimation (such as from stereo image information, asdiscussed above).

Conventionally, when using deep networks to achieve various tasks, analgorithm will be built for each task. Thus if it desired to recognizeautomobiles, then an algorithm will be built for that; if it is desiredto recognize faces, then an algorithm will be built for that; and thesealgorithms may be run simultaneously. If unlimited or high levels ofpower and computation resource are available, then such a configurationwill work well and get results; but in many scenarios, such as thescenario of a portable augmented reality system with a limited powersupply and limited processing capability in an embedded processor,computing and power resources can be relatively limited, and it may bedesirable to process certain aspects of the tasks together. Further,there is evidence that if one algorithm has knowledge from another, thenit makes the second algorithm better. For example, if one deep networkalgorithm knows about dogs and cats, knowledge transfer (also termed“domain adaptation”) from that may help another algorithm recognizeshoes better. So there is reason to have some kind of crosstalk betweenalgorithms during training and inference.

Further, there is a consideration related to algorithm design andmodification. Preferably if further capabilities are needed relative toan initial version of an algorithm, one won't need to completely rebuilda new one from scratch. The depicted Hydra architecture (272) may beutilized to address these challenges, as well as the computing and powerefficiency challenge, because as noted above, it is the case that thereare common aspects of certain computing processes that can be shared.For example, in the depicted Hydra architecture (272), inputs (270),such as image information from one or more cameras, may be brought intothe lower layers (268) where feature extraction on a relatively lowlevel may be conducted. For example, Gabor functions, derivatives ofGaussians, things that basically effect lines, edges, corners,colors—these are uniform for many problems at the low level. Thus,regardless of task variation, low level feature extraction can be thesame, whether it is the objective to extract cats, cars, or cows—andtherefore the computation related thereto can be shared. Hydraarchitecture (272) is a high-level paradigm which allows knowledgesharing across algorithms to make each better, it allows for featuresharing so that computation can be shared, reduced, and not redundant,and allows one to be able to expand the suite of capabilities withouthaving to rewrite everything—rather, new capabilities may be stackedupon the foundation with the existing capabilities.

Thus, as noted above, in the depicted embodiment, the Hydra architecturerepresents a deep neural network that has one unified pathway. Thebottom layers (268) of the network are shared, and they extract basicunits of visual primitives from input images and other inputs (270). Thesystem may be configured to go through a few layers of convolutions toextract edges, lines, contours, junctions, and the like. The basiccomponents that programmers used to feature-engineer, now become learnedby the deep network. As it turns out, these features are useful for manyalgorithms, whether the algorithm is face recognition, tracking, etc.Thus once the lower computational work has been done and there is ashared representation from images or other inputs into all of the otheralgorithms, then there can be individual pathways, one per problem. Thuson top of this shared representation, there is a pathway that leads toface recognition that is very specific to faces, there's a pathway thatleads to tracking that is very specific to SLAM, and so on for the other“heads” (264) of the architecture (272). With such an embodiment, onehas all of this shared computation that allows for multiplying additionsbasically, and on the other hand one has very specific pathways that areon top of the general knowledge and allow one to fine tune and findanswers to very specific questions.

Also of value with such a configuration is the fact that such neuralnetworks are designed so that the lower layers (268), which are closerto the input (270), require more computation, because at each layer ofcomputation, the system takes the original input and transforms it intosome other dimensional space where typically the dimensionality ofthings is reduced. So once the fifth layer of the network from thebottom layer is achieved, the amount of computation may be in the rangeof 20 time less than what was required in the lowest level (i.e.,because the input was much larger and much larger matrix multiplicationwas required). In one embodiment, by the time the system has extractedthe shared computation, it's fairly agnostic to the problems that needto be solved. A large portion of the computation of almost any algorithmhas been completed in the lower layers, so when new pathways are addedfor face recognition, tracking, depth, lighting, and the like, thesecontribute relatively little to the computational constraints—and thussuch an architecture provides plenty of capability for expansion.

In one embodiment, for the first few layers, they may be no pooling toretain the highest resolution data; mid layers may have poolingprocesses because at that point, super high resolution is not needed(for example, super high resolution is not needed to know where thewheel of a car is in a middle layer; one really just needs to know wherethe nut and bolt is located from the lower levels in high resolution,and then the image data can be significantly shrunk as it is passed tothe middle layers for location of the wheel of the car).

Further, once the network has all of the learned connections, everythingis loosely wired and the connections are advantageously learned throughthe data. The middle layers (266) may be configured to start learningparts, for example—object parts, face features, and the like; so ratherthan simple Gabor functions, the middle layers are processing morecomplex constructs (i.e., squiggly shapes, shading, etc.). Then as theprocess moves higher toward the top, there are split-offs into theunique head components (264), some of which may have many layers, andsome of which may have few. Again, the scalability and efficiency islargely due to the fact that a large portion, such as 90%, of theprocessing flops are within the lower layers (268), then a smallportion, such as 5% of the flops, are at the middle layers (266), andanother 5% is in the heads (264).

Such networks may be pre-trained using information that already exists.For example, in one embodiment, ImageNet, a large group (in the range of10 million) of images from a large group of classes (in the range of1,000) may be utilized to train all of the classes. In one embodiment,once it's trained, the top layer that distinguishes the classes may bethrown out, but all of the weights learned in the training process arekept.

Referring to FIG. 30A, a pair of coils (302, 304) is shown in aconfiguration with a particular radius and spacing therebetween, whichmay be known as a “Helmholtz coil.”

Helmholtz coils come in various configuration (here a pair a round coilsare shown) and are known for producing a relatively uniform magneticfield through a given volume, such as that depicted in FIG. 30B (306);magnetic field lines are shown with arrows about the cross sectionalviews of the coils (302, 304) of FIG. 30B. FIG. 3 illustrates athree-axis Helmholtz coil configuration wherein three pairs (310, 312,314) are orthogonally oriented as shown. Other variations of Helmholtzor Merritt coils, such as those featuring squared coils, also may beutilized to create predictable and relatively uniform magnetic fieldsthrough given volumes. In one embodiment, a Helmholtz type coil may beutilized to assist in calibrating the orientation determiningrelationship between two sensors operatively coupled to a head mountedcomponent (58) such as those described above. For example, referring toFIG. 30D, head mounted component (58) coupled to an IMU (102) andelectromagnetic field sensor (604), as described above, may be placedwithin a known magnetic field volume of a Helmholtz coil pair (302,304). With current applied through the coil pair (302, 304), the coilsmay be configured to generate magnetic fields at selectable frequencies.In one embodiment, the system may be configured to energize the coils ata direct current level to produce a directly-readable output from themagnetometer component of the IMU (102); then the coils may be energizedat an alternating current level, for example, to produce adirectly-readable output from the electromagnetic localization receivercoil (604). Since those applied fields in such a configuration aregenerated by the same physical coils (302, 304), they are registeredwith each other and we know that the fields must have the sameorientation. Thus we may read the values from the IMU (102) andelectromagnetic field sensor (604) and directly measure a calibrationwhich may be utilized to characterize any difference in orientationreadings between the two devices (102, 604) in three dimensions—thusproviding a usable calibration between the two for runtime. In oneembodiment, the head mounted component (58) may be electromechanicallyreoriented for further testing relative to the coil set (302, 304). Inanother embodiment, the coil set (302, 304) may be electromechanicallyreoriented for further testing relative to the head mounted component(58). In another embodiment, the head mounted component (58) and coilset (302, 304) may be electromechanically reorientable relative to eachother. In another embodiment, a three-axis Helmholtz coil, such as thatdepicted in FIG. 30C, or other more sophisticated magnetic fieldproducing coil, may be utilized to generate magnetic fields andcomponents without the need for reorientation of the head mountedcomponent (58) relative to the coil set (302, 304) for additionaltesting data.

Referring to FIG. 30E, the system or subsystem being utilized in suchcalibration configurations to produce a predictable magnetic field, suchas a pair of coils (302, 304) in a Helmholtz type of configuration, mayhave one or more optical fiducials (316) coupled thereto, such that theone or more cameras (124) which may comprise the head mounted component(58) may view such fiducials. Such a configuration provides anopportunity to ensure that the electromagnetic sensing subsystem isaligned in a known way with the cameras. In other words, with such aconfiguration, one has optical fiducials physically coupled or anchoredto the magnetic field generating device in a known or measured fashion(for example, an articulated coordinate measurement machine may beutilized to establish the precise X, Y, Z coordinates of each fiduciallocation 316); the head mounted component (58) may be placed inside ofthe testing volume and exposed to the magnetic field, while the cameras(124) of the head mounted component (58) observe one or more fiducials(316) and thus calibrate the extrinsics of the magnetic field sensorsand the cameras (because the magnetic field generator is attached to thefiducials that the cameras are observing). The optical fiducials (316)may comprise flat features such as checkerboards, aruco markers,textured or otherwise three-dimensional features. The optical fiducialsmay also be dynamic, such as in a configuration wherein small displays,such as LCD displays, are utilized; they may be static and printed out;they may be etched with lasers or chemistry into a substrate material;they may comprise coatings or anodizing or other features recognizableby the cameras (124). In a factory calibration setting, a plurality ofcalibration systems, such as those described herein, may be locatedadjacent one another, and may be timed such that adjacent systems do notproduce magnetic fields that would interfere with readings at anadjacent system. In one embodiment a group of calibration stations maybetime sequenced; in another embodiment every other, or every second, orevery third, and so on, may be simultaneously operated to providefunctional separation.

FIG. 31 illustrates a simplified computer system 3100 according to anembodiment described herein. Computer system 3100 as illustrated in FIG.31 may be incorporated into devices described herein. FIG. 31 provides aschematic illustration of one embodiment of computer system 3100 thatcan perform some or all of the steps of the methods provided by variousembodiments. It should be noted that FIG. 31 is meant only to provide ageneralized illustration of various components, any or all of which maybe utilized as appropriate. FIG. 31, therefore, broadly illustrates howindividual system elements may be implemented in a relatively separatedor relatively more integrated manner.

Computer system 3100 is shown comprising hardware elements that can beelectrically coupled via a bus 3105, or may otherwise be incommunication, as appropriate. The hardware elements may include one ormore processors 3110, including without limitation one or moregeneral-purpose processors and/or one or more special-purpose processorssuch as digital signal processing chips, graphics accelerationprocessors, and/or the like; one or more input devices 3115, which caninclude without limitation a mouse, a keyboard, a camera, and/or thelike; and one or more output devices 3120, which can include withoutlimitation a display device, a printer, and/or the like.

Computer system 3100 may further include and/or be in communication withone or more non-transitory storage devices 3125, which can comprise,without limitation, local and/or network accessible storage, and/or caninclude, without limitation, a disk drive, a drive array, an opticalstorage device, a solid-state storage device, such as a random accessmemory (“RAM”), and/or a read-only memory (“ROM”), which can beprogrammable, flash-updateable, and/or the like. Such storage devicesmay be configured to implement any appropriate data stores, includingwithout limitation, various file systems, database structures, and/orthe like.

Computer system 3100 might also include a communications subsystem 3119,which can include without limitation a modem, a network card (wirelessor wired), an infrared communication device, a wireless communicationdevice, and/or a chipset such as a Bluetooth™ device, an 802.11 device,a WiFi device, a WiMax device, cellular communication facilities, etc.,and/or the like. The communications subsystem 3119 may include one ormore input and/or output communication interfaces to permit data to beexchanged with a network such as the network described below to name oneexample, other computer systems, television, and/or any other devicesdescribed herein. Depending on the desired functionality and/or otherimplementation concerns, a portable electronic device or similar devicemay communicate image and/or other information via the communicationssubsystem 3119. In other embodiments, a portable electronic device,e.g., the first electronic device, may be incorporated into computersystem 3100, e.g., an electronic device as an input device 3115. In someembodiments, computer system 3100 will further comprise a working memory3135, which can include a RAM or ROM device, as described above.

Computer system 3100 also can include software elements, shown as beingcurrently located within the working memory 3135, including an operatingsystem 3140, device drivers, executable libraries, and/or other code,such as one or more application programs 3145, which may comprisecomputer programs provided by various embodiments, and/or may bedesigned to implement methods, and/or configure systems, provided byother embodiments, as described herein. Merely by way of example, one ormore procedures described with respect to the methods discussed above,might be implemented as code and/or instructions executable by acomputer and/or a processor within a computer; in an aspect, then, suchcode and/or instructions can be used to configure and/or adapt a generalpurpose computer or other device to perform one or more operations inaccordance with the described methods.

A set of these instructions and/or code may be stored on anon-transitory computer-readable storage medium, such as the storagedevice(s) 3125 described above. In some cases, the storage medium mightbe incorporated within a computer system, such as computer system 3100.In other embodiments, the storage medium might be separate from acomputer system e.g., a removable medium, such as a compact disc, and/orprovided in an installation package, such that the storage medium can beused to program, configure, and/or adapt a general purpose computer withthe instructions/code stored thereon. These instructions might take theform of executable code, which is executable by computer system 3100and/or might take the form of source and/or installable code, which,upon compilation and/or installation on computer system 3100 e.g., usingany of a variety of generally available compilers, installationprograms, compression/decompression utilities, etc., then takes the formof executable code.

Various exemplary embodiments of the invention are described herein.Reference is made to these examples in a non-limiting sense. They areprovided to illustrate more broadly applicable aspects of the invention.Various changes may be made to the invention described and equivalentsmay be substituted without departing from the true spirit and scope ofthe invention. In addition, many modifications may be made to adapt aparticular situation, material, composition of matter, process, processact(s) or step(s) to the objective(s), spirit or scope of the presentinvention. Further, as will be appreciated by those with skill in theart that each of the individual variations described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentinventions. All such modifications are intended to be within the scopeof claims associated with this disclosure.

The invention includes methods that may be performed using the subjectdevices. The methods may comprise the act of providing such a suitabledevice. Such provision may be performed by the end user. In other words,the “providing” act merely requires the end user obtain, access,approach, position, set-up, activate, power-up or otherwise act toprovide the requisite device in the subject method. Methods recitedherein may be carried out in any order of the recited events which islogically possible, as well as in the recited order of events.

Exemplary aspects of the invention, together with details regardingmaterial selection and manufacture have been set forth above. As forother details of the present invention, these may be appreciated inconnection with the above-referenced patents and publications as well asgenerally known or appreciated by those with skill in the art. The samemay hold true with respect to method-based aspects of the invention interms of additional acts as commonly or logically employed.

In addition, though the invention has been described in reference toseveral examples optionally incorporating various features, theinvention is not to be limited to that which is described or indicatedas contemplated with respect to each variation of the invention. Variouschanges may be made to the invention described and equivalents (whetherrecited herein or not included for the sake of some brevity) may besubstituted without departing from the true spirit and scope of theinvention. In addition, where a range of values is provided, it isunderstood that every intervening value, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the invention.

Also, it is contemplated that any optional feature of the inventivevariations described may be set forth and claimed independently, or incombination with any one or more of the features described herein.Reference to a singular item, includes the possibility that there areplural of the same items present. More specifically, as used herein andin claims associated hereto, the singular forms “a,” “an,” “said,” and“the” include plural referents unless the specifically stated otherwise.In other words, use of the articles allow for “at least one” of thesubject item in the description above as well as claims associated withthis disclosure. It is further noted that such claims may be drafted toexclude any optional element. As such, this statement is intended toserve as antecedent basis for use of such exclusive terminology as“solely,” “only” and the like in connection with the recitation of claimelements, or use of a “negative” limitation.

Without the use of such exclusive terminology, the term “comprising” inclaims associated with this disclosure shall allow for the inclusion ofany additional element—irrespective of whether a given number ofelements are enumerated in such claims, or the addition of a featurecould be regarded as transforming the nature of an element set forth insuch claims. Except as specifically defined herein, all technical andscientific terms used herein are to be given as broad a commonlyunderstood meaning as possible while maintaining claim validity.

The breadth of the present invention is not to be limited to theexamples provided and/or the subject specification, but rather only bythe scope of claim language associated with this disclosure.

What is claimed is:
 1. A method of resolving hemisphere ambiguity at asystem comprising one or more sensors, the method comprising: emitting,at a hand-held controller of the system, one or more magnetic fields;detecting, by one or more sensors positioned within a headset of thesystem, the one or more magnetic fields; determining a first positionand a first orientation of the hand-held controller within a firsthemisphere with respect to the headset based on the one or more magneticfields; determining a second position and a second orientation of thehand-held controller within a second hemisphere with respect to theheadset based on the one or more magnetic fields, wherein the secondhemisphere is diametrically opposite the first hemisphere with respectto the headset; determining a normal vector with respect to the headset,and a position vector identifying a position of the hand-held controllerwith respect to the headset in the first hemisphere; calculating adot-product of the normal vector and the position vector; determiningthat the first position and the first orientation of the hand-heldcontroller is accurate when a result of the dot-product is positive; anddetermining that the second position and the second orientation of thehand-held controller is accurate when the result of the dot-product isnegative.
 2. The method of claim 1, wherein the position vector isdefined at a coordinate frame of the headset.
 3. The method of claim 1,wherein the normal vector originates from the headset and extends at apredetermined angle from a horizontal line from the headset.
 4. Themethod of claim 3, wherein the predetermined angle is 45° angle downfrom the horizontal line from the headset.
 5. The method of claim 1,wherein when the result of the dot-product is positive, the firsthemisphere is identified as a front hemisphere with respect to theheadset and the second hemisphere is identified as a back hemispherewith respect to the headset.
 6. The method of claim 1, wherein when theresult of the dot-product is negative, the second hemisphere isidentified as a front hemisphere with respect to the headset and thefirst hemisphere is identified as a back hemisphere with respect to theheadset.
 7. The method of claim 1, further comprising: deliveringvirtual content to a display based on: the first position and the firstorientation when the result of the dot-product is positive; or thesecond position and the second orientation when the result of thedot-product is negative.
 8. The method of claim 1, wherein the system isan optical device.
 9. The method of claim 1, wherein the method isperformed during an initialization process of the headset.
 10. A systemcomprising: a hand-held controller comprising a magnetic fieldtransmitter configured to emit one or more magnetic fields; a headsetcomprising one or more magnetic field sensors configured to detect theone or more magnetic fields; a processor coupled to the headsetconfigured to perform operations including: determining a first positionand a first orientation of the hand-held controller within a firsthemisphere with respect to the headset based on the one or more magneticfields; determining a second position and a second orientation of thehand-held controller within a second hemisphere with respect to theheadset based on the one or more magnetic fields, wherein the secondhemisphere is diametrically opposite the first hemisphere with respectto the headset; determining a normal vector with respect to the headset,and a position vector identifying a position of the hand-held controllerwith respect to the headset in the first hemisphere; calculating adot-product of the normal vector and the position vector; determiningthat the first position and the first orientation of the hand-heldcontroller is accurate when a result of the dot-product is positive; anddetermining that the second position and the second orientation of thehand-held controller is accurate when the result of the dot-product isnegative.
 11. The system of claim 10, wherein the position vector isdefined at a coordinate frame of the headset.
 12. The system of claim10, wherein the normal vector originates from the headset and extends ata predetermined angle from a horizontal line from the headset.
 13. Thesystem of claim 12, wherein the predetermined angle is 45° angle downfrom the horizontal line from the headset.
 14. The system of claim 10,wherein when the result of the dot-product is positive, the firsthemisphere is identified as a front hemisphere with respect to theheadset and the second hemisphere is identified as a back hemispherewith respect to the headset.
 15. The system of claim 10, wherein whenthe result of the dot-product is negative, the second hemisphere isidentified as a front hemisphere with respect to the headset and thefirst hemisphere is identified as a back hemisphere with respect to theheadset.
 16. The system of claim 10, wherein the processor is furtherconfigured to perform operations including: delivering virtual contentto a display based on: the first position and the first orientation whenthe result of the dot-product is positive; or the second position andthe second orientation when the result of the dot-product is negative.17. The system of claim 10, wherein the system is an optical device. 18.The system of claim 10, wherein the operations are performed during aninitialization process of the headset.
 19. A computer-program productembodied in a non-transitory computer-readable medium, thecomputer-readable medium having stored thereon a sequence ofinstructions which, when executed by a processor, causes the processorto execute a method for resolving hemisphere ambiguity at a systemcomprising one or more sensors comprising: emitting, at a hand-heldcontroller of the system, one or more magnetic fields; detecting, by oneor more sensors positioned within a headset of the system, the one ormore magnetic fields; determining a first position and a firstorientation of the hand-held controller within a first hemisphere withrespect to the headset based on the one or more magnetic fields;determining a second position and a second orientation of the hand-heldcontroller within a second hemisphere with respect to the headset basedon the one or more magnetic fields, wherein the second hemisphere isdiametrically opposite the first hemisphere with respect to the headset;determining a normal vector with respect to the headset, and a positionvector identifying a position of the hand-held controller with respectto the headset in the first hemisphere; calculating a dot-product of thenormal vector and the position vector; determining that the firstposition and the first orientation of the hand-held controller isaccurate when a result of the dot-product is positive; and determiningthat the second position and the second orientation of the hand-heldcontroller is accurate when the result of the dot-product is negative.